Best AI Photo Editor

Best AI Photo Editor — hands-on reviews, top picks, pricing, pros and cons and a practical how-to guide on Aizhi.

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  Dropbox Carousel

  Dropbox Carousel was a photo and video management app offered by Dropbox. The third-party native app, available on Android and iOS, allowed users to store, manage, and organize photos. Photos were organized by date, time and event and backed up on Dropbox. It competed in this space against other online photo storage services such as Google's Google Photos, Apple's iCloud, and Yahoo's Flickr. Chris Lee, Dropbox's head of product development for Carousel described the app as an add-on to Dropbox, a “dedicated experience for photos and videos” and a space for “reliving personal memories”. == History == Mailbox founder, Gentry Underwood unveiled Carousel at a gathering in San Francisco on April 9, 2014. Much of the features in Carousel come from Snapjoy, a photo start-up, that Dropbox acquired on December 19, 2012. When Carousel was launched, it marked amongst many others, a series of acquisitions made by Dropbox to prep up before opening its stock for public offering. The acquisitions would help demonstrate its expansive product offerings pitching potential profitability to investors. In December 2015, Dropbox announced that Carousel would be shut down and some Carousel features would be integrated into the primary Dropbox application. On March 31, 2016, Carousel was deactivated. == Features == Carousel prompted users to free local storage once it had synced and backed-up local photos to the cloud. Flashback was a feature (enabled by default) that showed past photos or videos taken the same day, a year, or some years back. Flashback used an algorithm designed to identify human faces - resulting in greater likelihood of the user's picture or people in the user's close circle appearing. A scrollable timeline, which was earlier a scroll wheel, at the bottom let the user scroll to photo(s) at a specific date with a finger swipe.

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  Apptek

  Applications Technology (AppTek) is a U.S. company headquartered in McLean, Virginia that specializes in artificial intelligence and machine learning for human language technologies. The company provides both managed and professional services for natural language processing (NLP) technologies including automatic speech recognition (ASR), neural machine translation (MT), natural-language understanding (NLU) and neural speech synthesis. AppTek's Head of Science, Prof. Dr. -Ing Hermann Ney, was awarded the IEEE James L. Flanagan Speech and Audio Processing Award in 2019 and the ISCA Medal for Scientific Achievement in 2021 for his work in natural language processing. == History == AppTek was acquired in 1998 by Lernout & Hauspie (at the time a NASDAQ publicly traded company), AppTek organized a management buy-out and went private again in 2001. In 2014, the company sold its hybrid machine translation technology to eBay and has since rebuilt the platform to modern neural-based approaches for machine translation. In 2020, SOSi acquired non-controlling interest in AppTek and became an exclusive reseller of AppTek products for U.S. federal, state, and local government entities.

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  Bitstrips

  Bitstrips, Inc. was a Canadian media and technology company based in Toronto, founded in 2007 by Jacob Blackstock, David Kennedy, Shahan Panth, Dorian Baldwin, and Jesse Brown. The company created and offered a web application, Bitstrips.com, which allowed users to create comic strips using personalized avatars, and preset templates and poses. Brown and Blackstock explained that the service was meant to enable self-expression without the need to have artistic skills. Bitstrips was first presented in 2008 at South by Southwest in Austin, Texas, and the service later piloted and launched a version designed for use as educational software. The service achieved increasing prominence following the launch of versions for Facebook and mobile platforms. In 2014, Bitstrips launched a spin-off app known as Bitmoji, which allows users to create personalized stickers for use in instant messaging. In July 2016, Snapchat Inc. announced that it had acquired the company; the Bitstrips comic service was shut down, but Bitmoji remains operational, and has subsequently been given greater prominence within Snapchat's overall platform. == History == Bitstrips was co-developed by Toronto-based comic artist Jacob Blackstock and his high school friend, journalist Jesse Brown. The service was originally envisioned as a means to allow anyone to create their own comic strip without needing artistic skills. Brown explained that "it's so difficult and time-consuming to tell a story in comic book form, drawing the same characters again and again in these tiny little panels, and just the amount of craftsmanship required. And even if you can do it well, which I never could, it takes years to make a story." Brown stated that the service would be "groundwork for a whole new way to communicate", and went as far as describing the service as being a "YouTube for comics". Blackstock explained that the concept of Bitstrips was influenced by his own use of comics as a form of socialization; a student, Blackstock and his friends drew comics featuring each other and shared them during classes. He felt that Bitstrips was a "medium for self-expression", stating that "It's not just about you making the comics, but since you and your friends star in these comics, it's like you're the medium. The visual nature of comics just speaks so much louder than text." The service was publicly unveiled at South by Southwest in 2008. In 2009, the service introduced a version oriented towards the educational market, Bitstrips for Schools, which was initially piloted at a number of schools in Ontario. The service was praised by educators for being engaging to students, especially within language classes. Brown noted that students were using the service to create comics outside of class as well, stating that it was "so gratifying and shocking what people do with your tool to make their own stories in ways that you never would have anticipated. Some of them are just brilliant." In December 2012, Bitstrips launched a version for Facebook; by July 2013, Bitstrips had 10 million unique users on Facebook, having created over 50 million comics. In October 2013, Bitstrips launched a mobile app; in two months, Bitstrips became a top-downloaded app in 40 countries, and over 30 million avatars had been created with it. In November 2013, Bitstrips secured a round of funding from Horizons Ventures and Li Ka-shing. In October 2014, Bitstrips launched Bitmoji, a spin-off app that allows users to create stickers featuring Bitstrips characters in various templates. In July 2016, following unconfirmed reports earlier in the year, Snapchat Inc. announced that it had acquired Bitstrips. The company's staff continue to operate out of Toronto, but the original Bitstrips comic service was shut down in favour of focusing exclusively on Bitmoji, leaving many Bitstrips users to call for a reboot of the comic service.

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  Blobotics

  Blobotics is a term describing research into chemical-based computer processors based on ions rather than electrons. Andrew Adamatzky, a computer scientist at the University of the West of England, Bristol used the term in an article in New Scientist March 28, 2005 [1]. The aim is to create 'liquid logic gates' which would be 'infinitely reconfigurable and self-healing'. The process relies on the Belousov–Zhabotinsky reaction, a repeating cycle of three separate sets of reactions. Such a processor could form the basis of a robot which, using artificial sensors, interact with its surroundings in a way which mimics living creatures. The coining of the term was featured by ABC radio in Australia [2].

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  Richardson–Lucy deconvolution

  The Richardson–Lucy algorithm, also known as Lucy–Richardson deconvolution, is an iterative procedure for recovering an underlying image that has been blurred by a known point spread function. It was named after William Richardson and Leon B. Lucy, who described it independently. == Description == When an image is produced using an optical system and detected using photographic film, a charge-coupled device or a CMOS sensor, for example, it is inevitably blurred, with an ideal point source not appearing as a point but being spread out into what is known as the point spread function. Extended sources can be decomposed into the sum of many individual point sources, thus the observed image can be represented in terms of a transition matrix p operating on an underlying image: d i = ∑ j p i , j u j , {\displaystyle d_{i}=\sum _{j}p_{i,j}u_{j},} where u j {\displaystyle u_{j}} is the intensity of the underlying image at pixel j {\displaystyle j} , and d i {\displaystyle d_{i}} is the detected intensity at pixel i {\displaystyle i} . In general, a matrix whose elements are p i , j {\displaystyle p_{i,j}} describes the portion of light from source pixel j that is detected in pixel i. In most good optical systems (or in general, linear systems that are described as shift-invariant) the transfer function p can be expressed simply in terms of the spatial offset between the source pixel j and the observation pixel i: p i , j = P ( i − j ) , {\displaystyle p_{i,j}=P(i-j),} where P ( Δ i ) {\displaystyle P(\Delta i)} is called a point spread function. In that case the above equation becomes a convolution. This has been written for one spatial dimension, but most imaging systems are two-dimensional, with the source, detected image, and point spread function all having two indices. So a two-dimensional detected image is a convolution of the underlying image with a two-dimensional point spread function P ( Δ x , Δ y ) {\displaystyle P(\Delta x,\Delta y)} plus added detection noise. In order to estimate u j {\displaystyle u_{j}} given the observed d i {\displaystyle d_{i}} and a known P ( Δ i x , Δ j y ) {\displaystyle P(\Delta i_{x},\Delta j_{y})} , the following iterative procedure is employed in which the estimate of u j {\displaystyle u_{j}} (called u ^ j ( t ) {\displaystyle {\hat {u}}_{j}^{(t)}} ) for iteration number t is updated as follows: u ^ j ( t + 1 ) = u ^ j ( t ) ∑ i d i c i p i j , {\displaystyle {\hat {u}}_{j}^{(t+1)}={\hat {u}}_{j}^{(t)}\sum _{i}{\frac {d_{i}}{c_{i}}}p_{ij},} where c i = ∑ j p i j u ^ j ( t ) , {\displaystyle c_{i}=\sum _{j}p_{ij}{\hat {u}}_{j}^{(t)},} and ∑ j p i j = 1 {\displaystyle \sum _{j}p_{ij}=1} is assumed. It has been shown empirically that if this iteration converges, it converges to the maximum likelihood solution for u j {\displaystyle u_{j}} . Writing this more generally for two (or more) dimensions in terms of convolution with a point spread function P: u ^ ( t + 1 ) = u ^ ( t ) ⋅ ( d u ^ ( t ) ⊗ P ⊗ P ∗ ) , {\displaystyle {\hat {u}}^{(t+1)}={\hat {u}}^{(t)}\cdot \left({\frac {d}{{\hat {u}}^{(t)}\otimes P}}\otimes P^{}\right),} where the division and multiplication are element-wise, ⊗ {\displaystyle \otimes } indicates a 2D convolution, and P ∗ {\displaystyle P^{}} is the mirrored point spread function, or the inverse Fourier transform of the Hermitian transpose of the optical transfer function. In problems where the point spread function p i j {\displaystyle p_{ij}} is not known a priori, a modification of the Richardson–Lucy algorithm has been proposed, in order to accomplish blind deconvolution. == Derivation == In the context of fluorescence microscopy, the probability of measuring a set of number of photons (or digitalization counts proportional to detected light) m = [ m 0 , … , m K ] {\displaystyle \mathbf {m} =[m_{0},\dots ,m_{K}]} for expected values E = [ E 0 , … , E K ] {\displaystyle \mathbf {E} =[E_{0},\dots ,E_{K}]} for a detector with K + 1 {\displaystyle K+1} pixels is given by P ( m ∣ E ) = ∏ i K Poisson ⁡ ( E i ) = ∏ i K E i m i e − E i m i ! . {\displaystyle P(\mathbf {m} \mid \mathbf {E} )=\prod _{i}^{K}\operatorname {Poisson} (E_{i})=\prod _{i}^{K}{\frac {E_{i}^{m_{i}}e^{-E_{i}}}{m_{i}!}}.} Since in the context of maximum-likelihood estimation the aim is to locate the maximum of the likelihood function without concern for its absolute value, it is convenient to work with ln ⁡ ( P ) {\displaystyle \ln(P)} : ln ⁡ P ( m ∣ E ) = ∑ i K [ ( m i ln ⁡ E i − E i ) − ln ⁡ ( m i ! ) ] . {\displaystyle \ln P(\mathbf {m} \mid \mathbf {E} )=\sum _{i}^{K}[(m_{i}\ln E_{i}-E_{i})-\ln(m_{i}!)].} Moreover, since ln ⁡ ( m i ! ) {\displaystyle \ln(m_{i}!)} is a constant, it does not give any additional information regarding the position of the maximum, so consider α ( m ∣ E ) = ∑ i K [ m i ln ⁡ E i − E i ] , {\displaystyle \alpha (\mathbf {m} \mid \mathbf {E} )=\sum _{i}^{K}[m_{i}\ln E_{i}-E_{i}],} where α {\displaystyle \alpha } is something that shares the same maximum position as P ( m ∣ E ) {\displaystyle P(\mathbf {m} \mid \mathbf {E} )} . Now consider that E {\displaystyle \mathbf {E} } comes from a ground truth x {\displaystyle \mathbf {x} } and a measurement H {\displaystyle \mathbf {H} } which is assumed to be linear. Then E = H x , {\displaystyle \mathbf {E} =\mathbf {H} \mathbf {x} ,} where a matrix multiplication is implied. This can also be written in the form E m = ∑ n K H m n x n , {\displaystyle E_{m}=\sum _{n}^{K}H_{mn}x_{n},} where it can be seen how H {\displaystyle H} mixes or blurs the ground truth. It can also be shown that the derivative of an element of E {\displaystyle \mathbf {E} } , ( E i ) {\displaystyle (E_{i})} with respect to some other element of x j {\displaystyle x_{j}} can be written as It is easy to see this by writing a matrix H {\displaystyle \mathbf {H} } of, say, 5 × 5 and two arrays E {\displaystyle \mathbf {E} } and x {\displaystyle \mathbf {x} } of 5 elements and check it. This last equation can be interpreted as how much one element of x {\displaystyle \mathbf {x} } , say element i {\displaystyle i} , influences the other elements j ≠ i {\displaystyle j\neq i} (and of course the case i = j {\displaystyle i=j} is also taken into account). For example, in a typical case an element of the ground truth x {\displaystyle \mathbf {x} } will influence nearby elements in E {\displaystyle \mathbf {E} } but not the very distant ones (a value of 0 {\displaystyle 0} is expected on those matrix elements). Now, the key and arbitrary step: x {\displaystyle \mathbf {x} } is not known but may be estimated by x ^ {\displaystyle {\hat {\mathbf {x} }}} . Let's call x ^ old {\displaystyle {\hat {\mathbf {x} }}_{\text{old}}} and x ^ new {\displaystyle {\hat {\mathbf {x} }}_{\text{new}}} the estimated ground truths while using the RL algorithm, where the hat symbol is used to distinguish ground truth from estimator of the ground truth where ∂ ∂ x {\displaystyle {\frac {\partial }{\partial \mathbf {x} }}} stands for a K {\displaystyle K} -dimensional gradient. Performing the partial derivative of α ( m ∣ E ( x ) ) {\displaystyle \alpha (\mathbf {m} \mid \mathbf {E} (\mathbf {x} ))} yields the following expression: ∂ α ( m ∣ E ( x ) ) ∂ x j = ∂ ∂ x j ∑ i K [ m i ln ⁡ E i − E i ] = ∑ i K [ m i E i ∂ ∂ x j E i − ∂ ∂ x j E i ] = ∑ i K ∂ E i ∂ x j [ m i E i − 1 ] . {\displaystyle {\frac {\partial \alpha (\mathbf {m} \mid \mathbf {E} (\mathbf {x} ))}{\partial x_{j}}}={\frac {\partial }{\partial x_{j}}}\sum _{i}^{K}[m_{i}\ln E_{i}-E_{i}]=\sum _{i}^{K}\left[{\frac {m_{i}}{E_{i}}}{\frac {\partial }{\partial x_{j}}}E_{i}-{\frac {\partial }{\partial x_{j}}}E_{i}\right]=\sum _{i}^{K}{\frac {\partial E_{i}}{\partial x_{j}}}\left[{\frac {m_{i}}{E_{i}}}-1\right].} By substituting (1), it follows that ∂ α ( m ∣ E ( x ) ) ∂ x j = ∑ i K H i j [ m i E i − 1 ] . {\displaystyle {\frac {\partial \alpha (\mathbf {m} \mid \mathbf {E} (\mathbf {x} ))}{\partial x_{j}}}=\sum _{i}^{K}H_{ij}\left[{\frac {m_{i}}{E_{i}}}-1\right].} Note that H j i T = H i j {\displaystyle H_{ji}^{T}=H_{ij}} by the definition of a matrix transpose. And hence Since this equation is true for all j {\displaystyle j} spanning all the elements from 1 {\displaystyle 1} to K {\displaystyle K} , these K {\displaystyle K} equations may be compactly rewritten as a single vectorial equation ∂ α ( m ∣ E ( x ) ) ∂ x = H T [ m E − 1 ] , {\displaystyle {\frac {\partial \alpha (\mathbf {m} \mid \mathbf {E} (\mathbf {x} ))}{\partial \mathbf {x} }}=\mathbf {H} ^{T}\left[{\frac {\mathbf {m} }{\mathbf {E} }}-\mathbf {1} \right],} where H T {\displaystyle \mathbf {H} ^{T}} is a matrix, and m {\displaystyle \mathbf {m} } , E {\displaystyle \mathbf {E} } and 1 {\displaystyle \mathbf {1} } are vectors. Now, as a seemingly arbitrary but key step, let where 1 {\displaystyle \mathbf {1} } is a vector of ones of size K {\displaystyle K} (same as m {\displaystyle \mathbf {m} } , E {\displaystyle \mathbf {E} } and x {\displaystyle \mathbf {x} } ), and the d

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  Mathematical morphology

  Mathematical morphology (MM) is a theory and technique for analyzing and processing geometrical structures. It's based on set theory, lattice theory, topology, and random functions. MM is most commonly applied to digital images, but it can be employed as well on graphs, surface meshes, solids, and many other spatial structures. Topological and geometrical continuous-space concepts such as size, shape, convexity, connectivity, and geodesic distance, were introduced by MM on both continuous and discrete spaces. MM is also the foundation of morphological image processing, which consists of a set of operators that transform images according to the above characterizations. The basic morphological operators are erosion, dilation, opening and closing. MM was originally developed for binary images, and was later extended to grayscale functions and images. The subsequent generalization to complete lattices is widely accepted today as MM's theoretical foundation. == History == Mathematical Morphology was developed in 1964 by the collaborative work of Georges Matheron and Jean Serra, at the École des Mines de Paris, France. Matheron supervised the PhD thesis of Serra, devoted to the quantification of mineral characteristics from thin cross sections, and this work resulted in a novel practical approach, as well as theoretical advancements in integral geometry and topology. In 1968, the Centre de Morphologie Mathématique was founded by the École des Mines de Paris in Fontainebleau, France, led by Matheron and Serra. During the rest of the 1960s and most of the 1970s, MM dealt essentially with binary images, treated as sets, and generated a large number of binary operators and techniques: Hit-or-miss transform, dilation, erosion, opening, closing, granulometry, thinning, skeletonization, ultimate erosion, conditional bisector, and others. A random approach was also developed, based on novel image models. Most of the work in that period was developed in Fontainebleau. From the mid-1970s to mid-1980s, MM was generalized to grayscale functions and images as well. Besides extending the main concepts (such as dilation, erosion, etc.) to functions, this generalization yielded new operators, such as morphological gradients, top-hat transform and the Watershed (MM's main segmentation approach). In the 1980s and 1990s, MM gained a wider recognition, as research centers in several countries began to adopt and investigate the method. MM started to be applied to a large number of imaging problems and applications, especially in the field of non-linear filtering of noisy images. In 1986, Serra further generalized MM, this time to a theoretical framework based on complete lattices. This generalization brought flexibility to the theory, enabling its application to a much larger number of structures, including color images, video, graphs, meshes, etc. At the same time, Matheron and Serra also formulated a theory for morphological filtering, based on the new lattice framework. The 1990s and 2000s also saw further theoretical advancements, including the concepts of connections and levelings. In 1993, the first International Symposium on Mathematical Morphology (ISMM) took place in Barcelona, Spain. Since then, ISMMs are organized every 2–3 years: Fontainebleau, France (1994); Atlanta, USA (1996); Amsterdam, Netherlands (1998); Palo Alto, CA, USA (2000); Sydney, Australia (2002); Paris, France (2005); Rio de Janeiro, Brazil (2007); Groningen, Netherlands (2009); Intra (Verbania), Italy (2011); Uppsala, Sweden (2013); Reykjavík, Iceland (2015); Fontainebleau, France (2017); and Saarbrücken, Germany (2019). =

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  Graphics suite

  A graphics suite is a software suite for graphics work that are distributed together. The programs are usually able to interact with each other on a higher level than the operating system would normally allow. There is no hard, fast rule regarding the programs to be included in a graphics application suite, but most will include at least a bitmap graphics editor and a vector graphics editor. In addition to these, the suite may contain VRML editors, animation editors, and morphing tools.

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  Morphological antialiasing

  Morphological antialiasing (MLAA) is a spatial anti-aliasing technique used in real-time computer graphics. It reduces artifacts, such as jaggies, when representing a high-resolution image at a lower resolution. MLAA is a post-process filtering which detects borders in the resulting image and then finds specific patterns in these. Anti-aliasing is achieved by blending pixels in these borders, according to the pattern they belong to and their position within the pattern. Introduced in 2009, MLAA was an early and influential example of anti-aliasing techniques done in post-processing, which makes them suitable for deferred shading. A similar method in this class is fast approximate anti-aliasing (FXAA). Temporal anti-aliasing, also a post-process, has become the most common anti-aliasing method for real-time rendering and video games. Enhanced subpixel morphological antialiasing, or SMAA, is an image-based GPU-based implementation of MLAA developed by Universidad de Zaragoza and Crytek.

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  Geometric hashing

  In computer science, geometric hashing is a method for efficiently finding two-dimensional objects represented by discrete points that have undergone an affine transformation, though extensions exist to other object representations and transformations. In an off-line step, the objects are encoded by treating each pair of points as a geometric basis. The remaining points can be represented in an invariant fashion with respect to this basis using two parameters. For each point, its quantized transformed coordinates are stored in the hash table as a key, and indices of the basis points as a value. Then a new pair of basis points is selected, and the process is repeated. In the on-line (recognition) step, randomly selected pairs of data points are considered as candidate bases. For each candidate basis, the remaining data points are encoded according to the basis and possible correspondences from the object are found in the previously constructed table. The candidate basis is accepted if a sufficiently large number of the data points index a consistent object basis. Geometric hashing was originally suggested in computer vision for object recognition in 2D and 3D, but later was applied to different problems such as structural alignment of proteins. == Geometric hashing in computer vision == Geometric hashing is a method used for object recognition. Let’s say that we want to check if a model image can be seen in an input image. This can be accomplished with geometric hashing. The method could be used to recognize one of the multiple objects in a base, in this case the hash table should store not only the pose information but also the index of object model in the base. === Example === For simplicity, this example will not use too many point features and assume that their descriptors are given by their coordinates only (in practice local descriptors such as SIFT could be used for indexing). ==== Training Phase ==== Find the model's feature points. Assume that 5 feature points are found in the model image with the coordinates ( 12 , 17 ) ; {\displaystyle (12,17);} ( 45 , 13 ) ; {\displaystyle (45,13);} ( 40 , 46 ) ; {\displaystyle (40,46);} ( 20 , 35 ) ; {\displaystyle (20,35);} ( 35 , 25 ) {\displaystyle (35,25)} , see the picture. Introduce a basis to describe the locations of the feature points. For 2D space and similarity transformation the basis is defined by a pair of points. The point of origin is placed in the middle of the segment connecting the two points (P2, P4 in our example), the x ′ {\displaystyle x'} axis is directed towards one of them, the y ′ {\displaystyle y'} is orthogonal and goes through the origin. The scale is selected such that absolute value of x ′ {\displaystyle x'} for both basis points is 1. Describe feature locations with respect to that basis, i.e. compute the projections to the new coordinate axes. The coordinates should be discretised to make recognition robust to noise, we take the bin size 0.25. We thus get the coordinates ( − 0.75 , − 1.25 ) ; {\displaystyle (-0.75,-1.25);} ( 1.00 , 0.00 ) ; {\displaystyle (1.00,0.00);} ( − 0.50 , 1.25 ) ; {\displaystyle (-0.50,1.25);} ( − 1.00 , 0.00 ) ; {\displaystyle (-1.00,0.00);} ( 0.00 , 0.25 ) {\displaystyle (0.00,0.25)} Store the basis in a hash table indexed by the features (only transformed coordinates in this case). If there were more objects to match with, we should also store the object number along with the basis pair. Repeat the process for a different basis pair (Step 2). It is needed to handle occlusions. Ideally, all the non-colinear pairs should be enumerated. We provide the hash table after two iterations, the pair (P1, P3) is selected for the second one. Hash Table: Most hash tables cannot have identical keys mapped to different values. So in real life one won’t encode basis keys (1.0, 0.0) and (-1.0, 0.0) in a hash table. ==== Recognition Phase ==== Find interesting feature points in the input image. Choose an arbitrary basis. If there isn't a suitable arbitrary basis, then it is likely that the input image does not contain the target object. Describe coordinates of the feature points in the new basis. Quantize obtained coordinates as it was done before. Compare all the transformed point features in the input image with the hash table. If the point features are identical or similar, then increase the count for the corresponding basis (and the type of object, if any). For each basis such that the count exceeds a certain threshold, verify the hypothesis that it corresponds to an image basis chosen in Step 2. Transfer the image coordinate system to the model one (for the supposed object) and try to match them. If successful, the object is found. Otherwise, go back to Step 2. === Finding mirrored pattern === It seems that this method is only capable of handling scaling, translation, and rotation. However, the input image may contain the object in mirror transform. Therefore, geometric hashing should be able to find the object, too. There are two ways to detect mirrored objects. For the vector graph, make the left side positive, and the right side negative. Multiplying the x position by -1 will give the same result. Use 3 points for the basis. This allows detecting mirror images (or objects). Actually, using 3 points for the basis is another approach for geometric hashing. === Geometric hashing in higher-dimensions === Similar to the example above, hashing applies to higher-dimensional data. For three-dimensional data points, three points are also needed for the basis. The first two points define the x-axis, and the third point defines the y-axis (with the first point). The z-axis is perpendicular to the created axis using the right-hand rule. Notice that the order of the points affects the resulting basis

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  International Speech Communication Association

  The International Speech Communication Association (ISCA) is a non-profit organization and one of the two main professional associations for speech communication science and technology, the other association being the IEEE Signal Processing Society. == Purpose == The purpose of the International Speech Communication Association (ISCA) is to promote the study and application of automatic speech processing, including speech recognition and synthesis, as well as related areas such as speaker recognition and speech compression. The association's activities cover all aspects of speech processing, including computational, linguistic, and theoretical aspects. The primary goal of the International Speech Communication Association (ISCA) is to advance the field of automatic speech processing and communication technology through research, education, and collaboration. By promoting the study and application of speech technologies such as speech recognition, speech synthesis, speaker recognition, and speech compression, ISCA aims to foster innovation and development in the areas of human-computer interaction, telecommunications, and multimedia applications. ISCA serves as a platform for researchers, academics, industry professionals, and students to exchange knowledge, share best practices, and foster interdisciplinary dialogue in the field of speech communication science. Through conferences, workshops, publications, and educational initiatives, ISCA seeks to enhance the understanding of speech processing mechanisms, improve the accuracy and efficiency of speech technologies, and explore new frontiers in the realm of human language communication. Furthermore, ISCA plays a crucial role in promoting international collaboration and networking among professionals in the speech communication community. By facilitating partnerships and cooperation between individuals and organizations worldwide, ISCA seeks to drive global progress in speech technology research and application, ultimately contributing to the advancement of communication systems, accessibility tools, and interactive interfaces that benefit society as a whole. == Conferences == ISCA organizes yearly the Interspeech conference. Most recent Interspeech: 2013 Lyon, France 2014 Singapore 2015 Dresden, Germany 2016 San Francisco, US 2017 Stockholm, Sweden 2018 Hyderabad, India 2019 Graz, Austria 2020 Shanghai, China (fully virtual) 2021 Brno, Czechia (hybrid) 2022 Incheon, South Korea 2023 Dublin, Ireland 2023 Kos Island, Greece Forthcoming Interspeech: 2025 Rotterdam, the Netherlands == ISCA board == The ISCA president for 2023-2025 is Odette Scharenborg. The vice president is Bhuvana Ramabhadran and the other members are professionals in the field. == History of ISCA == The precursor to Interspeech was a conference called Eurospeech, first held in 1989 and organised by Jean-Pierre Tubach. It was the conference of the European Speech Communication Association (ESCA), itself the precursor of the International Speech Communication Association (ISCA). A year later another conference on speech science and technology was started: the International Conference on Spoken Language Processing (ICSLP), which was founded in 1990 by Hiroya Fujisaki. The first ISCA (vs. ESCA) event was the merging of Eurospeech and ICSLP to create ICSLP-Interspeech, held in Beijing, China in 2000. This was followed by Eurospeech-Interspeech, which was held in Aalborg, Denmark in 2001. In 2007, the Eurospeech and ICSLP parts of the conference names were dropped and Interspeech became the name of the yearly conference (first Interspeech location: Antwerp, Belgium).

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  Real-time transcription

  Real-time transcription is the general term for transcription by court reporters using real-time text technologies to deliver computer text screens within a few seconds of the words being spoken. Specialist software allows participants in court hearings or depositions to make notes in the text and highlight portions for future reference. Real-time transcription is also used in the broadcasting environment where it is more commonly termed "captioning." == Career opportunities == Real-time reporting is used in a variety of industries, including entertainment, television, the Internet, and law. Specific careers include the following: Judicial reporters use a stenotype to provide instant transcripts on computer screens as a trial or deposition occurs. Communication access real-time translation (CART) reporters assist the hearing-impaired by transcribing spoken words, giving them personal access to the communications they need day to day. Television broadcast captioners use real-time reporting technology to allow hard-of-hearing or deaf people to see what is being said on live television broadcasts such as news, emergency broadcasts, sporting events, awards shows, and other programs. Internet information (or Webcast) reporters provide real-time reporting of sales meetings, press conferences, and other events, while simultaneously transmitting the transcripts to computers worldwide. Other rapid data entry positions. == History == Before the advent of the stenotype machine, court reporters wrote official trial transcripts by hand using a shorthand system of stenoforms that could later be translated into readable English. It often took eight years of training to learn this manual form of writing at the necessary speed. Walter Heironimus was among the first stenographers to make use of the stenotype machine during his work in the U.S. District Court system in New Jersey in 1935. A "transcript crisis" arose during the later half of the twentieth century due to the increasing volume of lawsuits. There were not enough number of court reporters to match the increasing number of trials. Not only were court reporters unavailable to attend many court proceedings, court transcripts were constantly late and the qualities varied. Some believed it was due to the non-interchangeability between court reporters, and others believed it was simply due to a labor shortage. In the meantime, magnetic audiotape recording, or known as electronic recording (ER) began to threaten all reporters' job since it could record long-hour courtroom trials and replace a court reporter's position in the courtroom. As a result, machine translation (MT) intended to serve as a solution for preventing ER from potentially replacing reporters' jobs. However, MT relied heavily on human labors operating behind the system and many started to question if it should be the right way to end the "transcript crisis." Later in 1964, set up by CIA, the Automatic Language Processing Advisory Committee (ALPAC) was set to review whether MT was capable of solving this crisis. They concluded that MT had failed to do so. Then Patrick O'Neill, a skilled and experienced court reporter, stayed to work on the stenotype-translation project with CIA and developed the prototype CAT system. After adopting the CAT system in court-reporting community, CAT was brought into the television broadcasting system, aiming to provide captions for the deaf or hard-of-hearing communities. In 1983, Linda Miller developed a further use for the CAT system. She successfully translated a lecture live on the television screen and provided a transcript for students. This technique is known as Computer-Aided Real-time Translation, or CART. == Court reporter == It is the court reporter's job to note down the exact words spoken by every participants during a court or deposition proceeding. Then court reporters will provide verbatim transcripts. The reason to have an official court transcript is that the real-time transcriptions allows attorneys and judges to have immediate access to the transcript. It also helps when there's a need to look up for information from the proceeding. Additionally, the deaf and the hard-of-hearing communities can also participate in the judicial process with the help of real-time transcriptions provided by court reporters. === Education and training === The required degree level for a court reporter to have is an Associate's degree or postsecondary certificate. In order to become a court reporter, more than 150 reporter training programs are provided at proprietary schools, community colleges, and four-year universities. After graduation, court reporters can choose to further pursue certifications to achieve a higher level of expertise and increase their marketability during a job search. In most states, Certificates of Proficiency from the NCRA or from state agencies are now required certificates for court reporters to have in order to qualify for appointments. The NCRA aims to set the national standard for the certification of court reporters, and since 1937 it has offered its certification program which is now accepted by 22 states instead of state licenses. Court reporter training programs include but not limited to: Training in rapid writing skill, or shorthand, which will enable students to record, with accuracy, at least 225 words per minute Training in typing, which will enable students to type at least 60 words per minute A general training in English, which covers aspects of grammar, word formation, punctuation, spelling and capitalization Taking Law related courses in order to understand the overall principles of civil and criminal law, legal terminology and common Latin phrases, rules of evidence, court procedures, the duties of court reporters, the ethics of the profession Visits to actual trials Taking courses in elementary anatomy and physiology and medical word study including medical prefixes, roots and suffixes. Other than official court reporters, who are assigned to and work for a particular court, other types of court reporters include free-lance reporter, who either works for a court reporting firm or self-employed. They are different from official court reporters in that they have the chances to work on a wider range of assignments and work on basis of hourly wage. Hearing reporters work at governmental agency hearings. Legislative reporters work in law-making bodies. The demand for reporters is not limited in just the court settings. Reporters are also needed in conferences, meetings, conventions, investigations, and a variety of industries with needs for employers with real-time data entry skills. == Non-English transcription == Transcription services are universally necessary, so it is not limited to the English language. A stenographer's ability to transcribe languages beyond only English is especially valuable as society as a whole becomes increasingly multilingual. Education in non-English transcription demands a comprehensive understanding of the given language. Phonetic differences between English and other languages are a particular challenge in carrying English transcription skills over into other languages. Stenography represents various sounds of a language in a formal system of shorthand, so differences within the sets of sounds that emerge in other languages require an alternative system of shorthand transcription. For example, the presence of many diphthongs and triphthongs in Spanish requires certain sounds to be distinguished that would not be present in transcribing English into shorthand. == Controversies == The usage of transcription in the context of linguistic discussions has been controversial. Typically, two kinds of linguistic records are considered to be scientifically relevant. First, linguistic records of general acoustic features, and secondly, records that only focuses on the distinctive phonemes of a language. While transcriptions are not entirely illegitimate, transcriptions without enough detailed commentary regarding any linguistic features, or transcriptions of poor quality resources, has a great chance of the content being misinterpreted. Besides misinterpretation, transcribers could also bring in cultural biases and ignorance that reflect onto their transcription. These instances may cause a disruption of reliability in the final real-time transcription, which could influence how the written utterance is seen as an evidence for a court-case. === Quality issues === Problems in the final resulting transcription can be caused by either the quality of the transcriber or the original source that is being transcribed. Transcribers can come from different levels of skill and training background. This makes the final transcription prone to poor quality, or if the transcription is being done by multiple people, lack of consistency in the content. If the source of the transcription is a recording, the problem may root back to the quality of the re

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  International Speech Communication Association

  The International Speech Communication Association (ISCA) is a non-profit organization and one of the two main professional associations for speech communication science and technology, the other association being the IEEE Signal Processing Society. == Purpose == The purpose of the International Speech Communication Association (ISCA) is to promote the study and application of automatic speech processing, including speech recognition and synthesis, as well as related areas such as speaker recognition and speech compression. The association's activities cover all aspects of speech processing, including computational, linguistic, and theoretical aspects. The primary goal of the International Speech Communication Association (ISCA) is to advance the field of automatic speech processing and communication technology through research, education, and collaboration. By promoting the study and application of speech technologies such as speech recognition, speech synthesis, speaker recognition, and speech compression, ISCA aims to foster innovation and development in the areas of human-computer interaction, telecommunications, and multimedia applications. ISCA serves as a platform for researchers, academics, industry professionals, and students to exchange knowledge, share best practices, and foster interdisciplinary dialogue in the field of speech communication science. Through conferences, workshops, publications, and educational initiatives, ISCA seeks to enhance the understanding of speech processing mechanisms, improve the accuracy and efficiency of speech technologies, and explore new frontiers in the realm of human language communication. Furthermore, ISCA plays a crucial role in promoting international collaboration and networking among professionals in the speech communication community. By facilitating partnerships and cooperation between individuals and organizations worldwide, ISCA seeks to drive global progress in speech technology research and application, ultimately contributing to the advancement of communication systems, accessibility tools, and interactive interfaces that benefit society as a whole. == Conferences == ISCA organizes yearly the Interspeech conference. Most recent Interspeech: 2013 Lyon, France 2014 Singapore 2015 Dresden, Germany 2016 San Francisco, US 2017 Stockholm, Sweden 2018 Hyderabad, India 2019 Graz, Austria 2020 Shanghai, China (fully virtual) 2021 Brno, Czechia (hybrid) 2022 Incheon, South Korea 2023 Dublin, Ireland 2023 Kos Island, Greece Forthcoming Interspeech: 2025 Rotterdam, the Netherlands == ISCA board == The ISCA president for 2023-2025 is Odette Scharenborg. The vice president is Bhuvana Ramabhadran and the other members are professionals in the field. == History of ISCA == The precursor to Interspeech was a conference called Eurospeech, first held in 1989 and organised by Jean-Pierre Tubach. It was the conference of the European Speech Communication Association (ESCA), itself the precursor of the International Speech Communication Association (ISCA). A year later another conference on speech science and technology was started: the International Conference on Spoken Language Processing (ICSLP), which was founded in 1990 by Hiroya Fujisaki. The first ISCA (vs. ESCA) event was the merging of Eurospeech and ICSLP to create ICSLP-Interspeech, held in Beijing, China in 2000. This was followed by Eurospeech-Interspeech, which was held in Aalborg, Denmark in 2001. In 2007, the Eurospeech and ICSLP parts of the conference names were dropped and Interspeech became the name of the yearly conference (first Interspeech location: Antwerp, Belgium).

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  Software agent

  In computer science, a software agent is a computer program that acts for a user or another program in a relationship of agency. The term agent is derived from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Some agents are colloquially known as bots, from robot. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a computer, such as a mobile device, e.g. Siri. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people (e.g. chatbots, human-robot interaction environments) may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form (see Asimo). Related and derived concepts include intelligent agents (in particular exhibiting some aspects of artificial intelligence, such as reasoning), autonomous agents (capable of modifying the methods of achieving their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that work together to achieve an objective that could not be accomplished by a single agent acting alone), and mobile agents (agents that can relocate their execution onto different processors). == Concepts == The basic attributes of an autonomous software agent are that agents: are not strictly invoked for a task, but activate themselves, may reside in wait status on a host, perceiving context, may get to run status on a host upon starting conditions, do not require interaction of user, may invoke other tasks including communication. The concept of an agent provides a method of describing a complex software entity that is capable of acting with a certain degree of autonomy in order to accomplish tasks on behalf of its host. But unlike objects, which are defined in terms of methods and attributes, an agent is defined in terms of its behavior. Various authors have proposed different definitions of agents, these commonly include concepts such as: persistence: code is not executed on demand but runs continuously and decides for itself when it should perform some activity; autonomy: agents have capabilities of task selection, prioritization, goal-directed behavior, decision-making without human intervention; social ability: agents are able to engage other components through some sort of communication and coordination, they may collaborate on a task; reactivity: agents perceive the context in which they operate and react to it appropriately. === Distinguishing agents from programs === All agents are programs, but not all programs are agents. Contrasting the term with related concepts may help clarify its meaning. Franklin & Graesser (1997) discuss four key notions that distinguish agents from arbitrary programs: reaction to the environment, autonomy, goal-orientation and persistence. === Intuitive distinguishing agents from objects === Agents are more autonomous than objects. Agents have flexible behavior: reactive, proactive, social. Agents have at least one thread of control but may have more. === Distinguishing agents from expert systems === Expert systems are not coupled to their environment. Expert systems are not designed for reactive, proactive behavior. Expert systems do not consider social ability. === Distinguishing intelligent software agents from intelligent agents in AI === Intelligent agents (also known as rational agents) are not just computer programs: they may also be machines, human beings, communities of human beings (such as firms) or anything that is capable of goal-directed behavior. == Impact of software agents == Software agents may offer various benefits to their end users by automating complex or repetitive tasks. However, there are organizational and cultural impacts of this technology that need to be considered prior to implementing software agents. === Organizational impact === === Work contentment and job satisfaction impact === People like to perform easy tasks providing the sensation of success unless the repetition of the simple tasking is affecting the overall output. In general implementing software agents to perform administrative requirements provides a substantial increase in work contentment, as administering their own work does never please the worker. The effort freed up serves for a higher degree of engagement in the substantial tasks of individual work. Hence, software agents may provide the basics to implement self-controlled work, relieved from hierarchical controls and interference. Such conditions may be secured by application of software agents for required formal support. === Cultural impact === The cultural effects of the implementation of software agents include trust affliction, skills erosion, privacy attrition and social detachment. Some users may not feel entirely comfortable fully delegating important tasks to software applications. Those who start relying solely on intelligent agents may lose important skills, for example, relating to information literacy. In order to act on a user's behalf, a software agent needs to have a complete understanding of a user's profile, including his/her personal preferences. This, in turn, may lead to unpredictable privacy issues. When users start relying on their software agents more, especially for communication activities, they may lose contact with other human users and look at the world with the eyes of their agents. These consequences are what agent researchers and users must consider when dealing with intelligent agent technologies. === History === The concept of an agent can be traced back to Hewitt's Actor Model (Hewitt, 1977) - "A self-contained, interactive and concurrently-executing object, possessing internal state and communication capability." To be more academic, software agent systems are a direct evolution of Multi-Agent Systems (MAS). MAS evolved from Distributed Artificial Intelligence (DAI), Distributed Problem Solving (DPS) and Parallel AI (PAI), thus inheriting all characteristics (good and bad) from DAI and AI. John Sculley's 1987 "Knowledge Navigator" video portrayed an image of a relationship between end-users and agents. Being an ideal first, this field experienced a series of unsuccessful top-down implementations, instead of a piece-by-piece, bottom-up approach. The range of agent types is now (from 1990) broad: WWW, search engines, etc. == Examples of intelligent software agents == === Buyer agents (shopping bots) === Buyer agents travel around a network (e.g. the internet) retrieving information about goods and services. These agents, also known as 'shopping bots', work very efficiently for commodity products such as CDs, books, electronic components, and other one-size-fits-all products. Buyer agents are typically optimized to allow for digital payment services used in e-commerce and traditional businesses. === User agents (personal agents) === User agents, or personal agents, are intelligent agents that take action on your behalf. In this category belong those intelligent agents that already perform, or will shortly perform, the following tasks: Check your e-mail, sort it according to the user's order of preference, and alert you when important emails arrive. Play computer games as your opponent or patrol game areas for you. Assemble customized news reports for you. There are several versions of these, including CNN. Find information for you on the subject of your choice. Fill out forms on the Web automatically for you, storing your information for future reference Scan Web pages looking for and highlighting text that constitutes the "important" part of the information there Discuss topics with you ranging from your deepest fears to sports Facilitate with online job search duties by scanning known job boards and sending the resume to opportunities who meet the desired criteria Profile synchronization across heterogeneous social networks === Monitoring-and-surveillance (predictive) agents === Monitoring and surveillance agents are used to observe and report on equipment, usually computer systems. The agents may keep track of company inventory levels, observe competitors' prices and relay them back to the company, watch stock manipulation by insider trading and rumors, etc. For example, NASA's Jet Propulsion Laboratory has an agent that monitors inventory, planning, schedules equipment orders to keep costs down, and manages food storage facilities. These agents usually monitor complex computer networks that can keep track of the configuration of each computer connected to the network. A special case of monitoring-and-surveillance agents are organizations of agents used to automate decision-making process during tactical operations. The agents monitor the status of assets (ammunition, weapons available, platforms for transport, etc.) and receive goals from hi

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  Abillion

  abillion was a mobile application helping users to find vegan and sustainable products. The platform allowed users to review plant-based, cruelty-free and sustainable products, while donating between 0.10 and $1 to nonprofit organisations for each review written. As of May 2023, the company claimed to have donated over $2.8M to various nonprofit organisations including Sea Shepherd and Mercy for Animals. The main objective of the company was to reach the number of one billion people following a vegan diet and lifestyle by 2030. == History == The American entrepreneur Vikas Garg founded the company in Singapore and the app was officially launched in May 2018. The start-up was first named 'abillionveg' and changed its name in 2020 to shorten it to 'abillion'. In 2019, the company raised $3M in its first round of funding (pre-Series A). In 2021, it raised $10M in its Series A funding. In February 2023, the company announced the launch of a community investment round, using the crowdfunding platform Wefunder, which reached a total of $500 000. In May 2023, it celebrated its 5th anniversary and reaching 1M downloads. In March 2026, the company announced that they would be closing down by the end of the month. == Awards == Using data from the reviews published by its users, abillion was awarding the most liked vegan products and brands. In May 2023, the company published a world Top 10 Best Plant Based Burgers, among the winning brands were Beyond Meat, NotCo and Sojasun.

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  Reference Software International

  Reference Software International, Inc. (RSI), was an American software developer active from 1985 to 1993 and based in Albuquerque, New Mexico, and San Francisco, California. The company released several productivity and reference software packages, including the Grammatik grammar checker, for MS-DOS. The company was acquired by WordPerfect Corporation in 1993. == History == === Background (1980–1985) === Reference Software International, Inc., was founded by Donald "Don" Emery and Bruce Wampler in 1985 in San Francisco, California. Both Wampler and Emery were college professors when they founded RSI: Wampler at the University of New Mexico as a professor of computer science and Emery a professor of marketing at San Francisco State University. After graduating from the University of Utah in around 1978, Wampler founded his first software company, Aspen Software, in Tijeras, New Mexico, in 1979. Wampler founded Aspen to develop an early spell checker software package, called Proofreader, for the TRS-80, licensing Random House's Webster's Unabridged Dictionary for the package's lexicon. In 1980, he began development on a grammar checker inspired by Writer's Workbench, a pioneering grammar checker for Unix systems. Wampler used Writer's Workbench heavily during the writer of his doctoral dissertation but disliked having to jump between the Apple II on which he composed the dissertation and the mainframe on which Writer's Workbench ran, and so wanted to develop a version of the latter for microcomputers. Wampler's work came to fruition as Grammatik in 1981, eventually ported to several other microcomputer platforms in the early 1980s. In 1983, by which point the company had 12 employees and sold a combined 80,000 units of Grammatik and Proofreader, Wampler sold Aspen to Dictronics, a software company best known for developing the Electronic Thesaurus, an early thesaurus program for microcomputers. Dictronics was in turn purchased by Wang Laboratories; according to Wampler, "Wang bought [Aspen] and sat on it. They did nothing with it". Wampler moved on to teach for the University of New Mexico, but, frustrated by Wang's inaction, got the urge to resurrect his work. In 1985, he was able to license back Grammatik and Proofreader from a small California-based software firm that had grandfathered rights to a forked version of both. In the same year, he met Emery, who, impressed by Wampler's, founded Reference Software International to market his software. RSI's research and development headquarters were based in Albuquerque, while the company's sales and marketing department was based in Walnut Creek, California. === Success (1985–1992) === In August 1985, RSI released their first product: the Random House Reference Set, a new version of Proofreader for the IBM Personal Computer and compatibles, revised to be a terminate-and-stay-resident program that ran atop other word processors such as WordStar or WordPerfect. At the time, Reference Set was the only such program on the market that functioned like this. RSI netted $114,000 from sales of Reference Set by the end of 1985. In June 1986, they released version 2.0 of Grammatik as Grammatik II for the PC. The latter was a breakout hit for RSI, receiving praise in the press (including technology journals such as PC Magazine) and RSI selling 1,000 units a month. In spring 1987, they released Reference Set II, which allowed users to import their own words into the built-in dictionary and added a thesaurus of 300,000 words. In November 1987, they released version 3.0 of Reference Set, which comprised two new field-specific dictionaries for the medical and legal professions. As well as the general Random House dictionary and thesaurus, it included Stedman's Medical Dictionary and Black's Law Dictionary. Emery consulted Paul Brest and Bob Jackson—professors of law at Stanford Law School and San Francisco State respectively—for the curation of the law dictionary; and Burton Grebin—at the time the executive director of Mount Saint Mary's Hospital—for the curation of the medical dictionary. In fall 1988, the company released Grammatik III, a total rewrite that made use of artificial intelligence to more accurately judge the grammar of sentences by breaking them down into a syntactic hierarchy. Grammatik III received universal acclaim, with Gloria Morris of InfoWorld calling it the apparent leader in the grammar checking field and Sandra Anderson of Mac Home Journal calling it "hands down ... the best of the industry" six years after its release. By 1989, the product had competitors in Correct Grammar by Lifetree Software and RightWriter by Rightsoft, Inc. By 1990, RSI achieved annual sales of $9.7 million. In the same year they released Grammatik IV, which was the first to offer direct integration with WordPerfect on both MS-DOS and Windows. In March 1992—by which point RSI had sold 1.5 million copies of Grammatik across all versions—the company released version 5 of the program, another rewrite that updated the lexicon further and added new functions such as word redundancy detection. Around the same time, the company introduced Easy Proof, a pared-down version of Grammatik intended for novice writers, students, and family computers. In 1991, the company was engaged in a trademark dispute with Systems Compatibility Corporation (SCC) of Chicago, Illinois, over the rights to the Software Toolkit title. Both companies had published software bundles bearing the name in the turn of the 1990s; SCC had published theirs first in 1988 and registered the trademark with the USPTO. SCC was granted a restraining order against RSI in January 1991. The following month, RSI agreed to rename their product, preventing a protracted legal battle. === Decline and acquisition (1992–1993) === By early 1992, RSI achieved annual sales of more than $13 million, employed 120 people, and had opened international offices in London, Belgium, and Antwerp to sell foreign versions of Reference Set and Grammatik. The company reached peak employment in the middle of 1992, with 140 employees. However, RSI's launch of six disparate titles in the year proved problematic for the company when they failed to sell as well as they had projected, and the company laid off employees by the dozens. By December 1992, only 71 employees were left, 32 from their San Francisco office. On the last day of 1992, RSI received an acquisition offer from WordPerfect Corporation, makers of the namesake word processor based in Orem, Utah. The deal was inked in January 1993, RSI's stakeholders receiving $19 million. The company's remaining employees were absorbed into WordPerfect in Orem. WordPerfect continued selling Grammatik as a standalone product for several years.

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