Sign Language Processing

Amit Moryossef (

Yoav Goldberg (


Signed languages (also known as sign languages) are languages that use the visual-gestural modality to convey meaning through manual articulations in combination with non-manual elements like the face and body. Similar to spoken languages, signed languages are natural languages governed by a set of linguistic rules (Sandler and Lillo-Martin 2006), both emerging through an abstract, protracted aging process and evolved without meticulous planning. Signed languages are not universal, or mutually intelligible, despite often having striking similarities among them. They are also distinct from spoken languages—i.e., American Sign Language (ASL) is not a visual form of English, rather its own unique language.

Sign Language Processing (Bragg et al. 2019; Yin et al. 2021) is an emerging field of artificial intelligence concerned with the automatic processing and analysis of sign language content. It is a subfield of both natural language processing (NLP) and computer vision (CV). Challenges in sign language processing frequently involve machine translation of sign language videos to spoken language text (sign language translation), from spoken language text (sign language production), or sign language recognition for sign language understanding.

Unfortunately, the latest advances in language-based artificial intelligence, like machine translation and personal assistants, expect a spoken language input (text or transcribed speech), which excludes around 200 different signed languages and up to 70 million deaf people (According to the World Federation of the Deaf).

One of the challenging aspects regarding the translation of signed languages compared to spoken languages is that while spoken languages usually have agreed upon written forms, signed languages do not. The lack of a written form makes the spoken language processing pipelines - which often start with audio-transcription before processing - incompatible with signed languages, forcing researchers to work directly on the raw video signal.

In this work, we describe the different representations used for sign language processing, as well as survey the various tasks and recent advances on them. We also make a comprehensive list of existing datasets and make the ones available easy to load using a simple and standardized interface.

Sign Language Representations

As signed languages are conveyed through the visual-gestural modality, the most straightforward way to capture them is via video recording. However, as videos include more information than needed for modeling, and are expensive to record, store, and transmit, a lower-dimensionality representation has been sought after.

One such representation is human poses, either recorded with motion capture technologies, or estimated from videos using pose estimation techniques. Accurate full-body human poses can include all the relevant information for sign language processing (manual or non-manual), except for visual cues such as props.

Another representation system is sign language notation. Despite the fact that several notation systems have been proposed to capture the phonetics of signed languages, no writing system has been adopted widely enough by any sign language community that it could be considered the “written form” of a given sign language. There are various universal notation systems—SignWriting (Sutton 1990), HamNoSys (Prillwitz and Zienert 1990)—and various language specific notation systems—Stokoe notation (Stokoe Jr 2005) and si5s for American Sign Language, SWL (Bergman 1977) for Swedish Sign Language, etc.

Making an abstraction over the phonetics of sign language, glossing is a widely used practice to “transcribe” a video sign-by-sign by assigning a unique identifier for every sign and possibly every variation of it. Unlike other representations previously discussed, as glosses represent the semantics of sign, they are language-specific, requiring a new glossary for every sign language.

The following table exemplifies the various representations for isolated signs. For this example, we use SignWriting as the notation system. Note that the same sign might have two unrelated glosses, and the same gloss might have multiple valid spoken language translations.

Video Pose Estimation Notation Gloss English Translation
What’s wrong?

We also demonstrate the various representations in the following figure for a continuous sign language video. To show the alignment of the annotations between the video and representations, we deconstruct the video into its individual frames. An English translation of this phrase could be: “What is your name?”


Sign Language Detection

Sign language detection (Borg and Camilleri 2019; Moryossef et al. 2020) is defined as the binary classification for any given frame of a video whether a person is using sign language or not.

Borg and Camilleri (2019) introduced the classification of frames taken from YouTube videos as either signing or not. They take a spatial and temporal approach based on VGG-16 (Simonyan and Zisserman 2015) CNN to encode each frame and use a GRU (Cho et al. 2014) to encode the sequence of frames in a window of 20 frames at 5fps. In addition to the raw frame, they also either encode optical flow history, aggregated motion history, or frame difference.

Moryossef et al. (2020) improved upon their method by performing sign language detection in real-time. They identified that sign language use involves movement of the body, and as such, designed a model that works on top of estimated human poses rather than directly on the video signal. They calculate the optical flow norm of every joint detected on the body and apply a shallow yet effective contextualized model to predict for every frame whether the person is signing or not.

While these works perform well on this task, well-annotated data, including interferences and distractors, is still lacking for real-world evaluation.

Sign Language Identification

Sign language identification (Gebre, Wittenburg, and Heskes 2013; Monteiro et al. 2016) is defined as the classification between two or more signed languages.

Gebre, Wittenburg, and Heskes (2013) found that a simple random-forest classifier can distinguish between British Sign Language (BSL) and Greek Sign Language (ENN) with a 95% F1 score. This finding is further supported by Monteiro et al. (2016), which manages to differentiate between British Sign Language and French Sign Language (Langue des Signes Française, LSF) with 98% F1 score in videos with static backgrounds, and between American Sign Language and British Sign Language with 70% F1 score for videos mined from popular video sharing sites. The authors attribute their success mainly to the different fingerspelling systems, which is two-handed in the case of BSL and one-handed in the case of ASL and LSF.

Sign Language Segmentation

Segmentation consists of detecting the frame boundaries for signs or phrases in videos to divide them into meaningful units. While the most canonical way of dividing a spoken language text is into a linear sequence of words, due to the simultaneity of sign language, the notion of a sign language “word” is ill-defined, and sign language cannot be fully modeled linearly.

Current methods resort to segmenting units loosely mapped to signed language units (Santemiz et al. 2009; Farag and Brock 2019; Bull, Gouiffès, and Braffort 2020), and do not leverage reliable linguistic predictors of sentence boundaries such as prosody in signed languages (i.e. pauses, sign duration, facial expressions, eye apertures) .

Bull, Gouiffès, and Braffort (2020) segment French Sign Language into subtitle-units by detecting the temporal boundaries of subtitles aligned with sign language videos.

Farag and Brock (2019) use a random forest classifier to distinguish frames containing words in Japanese Sign Language

TODO add Gul Varol’s works

Sign Language Recognition, Translation, and Production

Sign language translation is generally considered the task of translating between a video in sign language to spoken language text. Sign language production is the reverse process of producing a sign language video from spoken language text. Sign language recognition (Adaloglou et al. 2020) is the task of recognizing the discrete signs themselves in sign language (glosses).

In the following graph, we can see a fully connected pentagon where each node is a single data representation, and each directed edge represents the task of converting between one data representation to another.

We split the graph into two:

Language Agnostic Tasks Language Specific Tasks

In total, there are 20 tasks conceptually defined by this graph, with varying amounts of previous research. Every path between two nodes might or might not be valid, depending on how lossy the tasks in the path are.


Video-to-Pose—commonly known as pose estimation—is the task to detect human figures in images and videos, so that one could determine, for example, where someone’s elbow shows up in an image. It was shown (Vogler and Goldenstein 2005) that the face pose correlates with facial non-manual features like head direction.

This area has been thoroughly researched (Pishchulin et al. 2012; Chen et al. 2017; Cao et al. 2019; Güler, Neverova, and Kokkinos 2018) with objectives varying from predicting 2D / 3D poses to a selection of a small specific set of landmarks or a dense mesh of a person.

OpenPose (Cao et al. 2019; Simon et al. 2017; Cao et al. 2017; Wei et al. 2016) is the first multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) in 2D on single images. While their model can estimate the full pose directly from an image in a single inference, they also suggest a pipeline approach where first they estimate the body pose and then independently estimate the hands and face pose by acquiring higher resolution crops around those areas. Building on the slow pipeline approach, a single network whole body OpenPose model has been proposed (Hidalgo et al. 2019), which is faster and more accurate for the case of obtaining all keypoints. Additionally, with multiple angles of recording, OpenPose also offers keypoint triangulation in order to reconstruct the pose in 3D.

Güler, Neverova, and Kokkinos (2018) takes a different approach with DensePose. Instead of classifying for every keypoint which pixel is most likely, they suggest similarly to semantic segmentation, for each pixel to classify which body part it belongs to. Then, for each pixel, knowing the body part, they predict where that pixel is on the body part relative to a 2D projection of a representative body model. This approach results in the reconstruction of the full-body mesh and allows sampling to find specific keypoints similar to OpenPose.

However, 2D human poses might not be sufficient to fully understand the position and orientation of landmarks in space, and applying pose estimation per-frame does not take the video temporal movement information into account, especially in cases of rapid movement, which contain motion blur.

Pavllo et al. (2019) developed two methods to convert between 2D poses to 3D poses. The first, a supervised method, was trained to use the temporal information between frames to predict the missing Z-axis. The second, an unsupervised method, leveraging the fact that the 2D poses are merely a projection of an unknown 3D pose and train a model to estimate the 3D pose and back-project to the input 2D poses. This back-projection is a deterministic process, and as such, it applies constraints on the 3D pose encoder. Zelinka and Kanis (2020) follow a similar process and adds a constraint for bones to stay of a fixed length between frames.

Panteleris, Oikonomidis, and Argyros (2018) suggest converting the 2D poses to 3D using inverse kinematics (IK), a process taken from computer animation and robotics to calculate the variable joint parameters needed to place the end of a kinematic chain, such as a robot manipulator or animation character’s skeleton in a given position and orientation relative to the start of the chain. Demonstrating their approach on hand pose estimation, they manually explicitly encode the constraints and limits of each joint, resulting in 26 degrees of freedom. Then, non-linear least-squares minimization fits a 3D model of the hand to the estimated 2D joint positions, recovering the 3D hand pose. This is similar to the back-projection used by Pavllo et al. (2019), except here, no temporal information is being used.

MediaPipe Holistic (Grishchenko and Bazarevsky 2020) attempts to solve the 3D pose estimation problem directly by taking a similar approach to OpenPose, having a pipeline system to estimate the body and then the face and hands. Unlike OpenPose, the estimated poses are in 3D, and the pose estimator runs in real-time on CPU, allowing for pose-based sign language models on low powered mobile devices. This pose estimation tool is widely available and built for Android, iOS, C++, Python, and the Web using Javascript.


Pose-to-Video, also known as motion-transfer or skeletal animation in the field of robotics and animation, is the conversion of a sequence of poses to a realistic-looking video. For sign language production, this is the final “rendering” to make the produced sign language look human.

Chan et al. (2019) demonstrates a semi-supervised approach where they take a set of videos, run pose-estimation with OpenPose (Cao et al. 2019), and learn an image-to-image translation (Isola et al. 2017) between the rendered skeleton and the original video. They demonstrate their approach on human dancing, where they can extract poses from a choreography, and render any person as if they were dancing that dance. They predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for a more realistic face synthesis, although still flawed.

Wang et al. (2018) suggest a similar method using DensePose (Güler, Neverova, and Kokkinos 2018) representations in addition to the OpenPose (Cao et al. 2019) ones. They formalize a different model, with various objectives to optimize for such as background-foreground separation and temporal coherence by using the previous two timestamps in the input.

Using the same method by Chan et al. (2019) on “Everybody Dance Now”, Giró-i-Nieto (2020) asks, “Can Everybody Sign Now”? They evaluate the generated videos by asking signers various tasks after watching them and comparing the signers’ ability to perform these tasks on the original videos, rendered pose videos, and reconstructed videos. They show that subjects prefer synthesized realistic videos over skeleton visualizations, and that out-of-the-box synthesis methods are not really effective enough, as subjects struggled to understand the reconstructed videos.

As a direct response, Saunders, Camgöz, and Bowden (2020b) shows that like in Chan et al. (2019), where an adversarial loss is added to specifically generate the face, adding a similar loss to the hand generation process yields high resolution, more photo-realistic continuous sign language videos.

Deepfakes is a technique to replace a person in an existing image or video with someone else’s likeness (Nguyen et al. 2019). This technique can be used to improve the unrealistic face synthesis, resulting from not face-specialized models, or even replace cartoon faces from animated 3D models.


Pose-to-Gloss—also known as sign language recognition—is the task of recognizing a sequence of signs from a sequence of poses.



Gloss-to-Pose—also known as sign language production—is the task to produce a sequence of poses that adequately represent a sequence of signs written as gloss.

To produce a sign language video, Stoll et al. (2018) constructs a lookup-table between glosses and sequences of 2D poses. They align all pose sequences at the neck joint of a reference skeleton and group all sequences belonging to the same gloss. Then, for each group, they apply dynamic time warping and average out all sequences in the group to construct the mean pose sequence. This approach suffers from not having an accurate set of poses aligned to the gloss and from unnatural motion transitions between glosses.

To alleviate the downsides of the previous work, Stoll et al. (2020) constructs a lookup-table of gloss to a group of sequences of poses rather than creating a mean pose sequence. They build a Motion Graph (Min and Chai 2012) - which is a Markov process that can be used to generate new motion sequences that are representative of real motion, and select the motion primitives (sequence of poses) per gloss with the highest transition probability. To smooth that sequence and reduce unnatural motion, they use Savitzky–Golay motion transition smoothing filter (Savitzky and Golay 1964).


Video-to-Gloss—also known as sign language recognition—is the task of recognizing a sequence of signs from a video.

For this recognition, Cui, Liu, and Zhang (2017) constructs a three-step optimization model. First, they train a video-to-gloss end-to-end model, where they encode the video using a spatio-temporal CNN encoder, and predict the gloss using a Connectionist Temporal Classification (CTC) (Graves et al. 2006). Then, from the CTC alignment and category proposal, they encode each gloss-level segment independently, trained to predict the gloss category, and use this gloss video segments encoding to optimize the sequence learning model.

Cihan Camgöz et al. (2018) fundamentally differ from that approach and opt to formulate this problem as if it is a natural-language translation problem. They encode each video frame using AlexNet (Krizhevsky, Sutskever, and Hinton 2012), initialized using weights that were trained on ImageNet (Deng et al. 2009). Then they apply a GRU encoder-decoder architecture with Luong Attention (Luong, Pham, and Manning 2015) to generate the gloss. In follow-up work, Camgöz et al. (2020b) use a transformer encoder (Vaswani et al. 2017) to replace the GRU and use a CTC to decode the gloss. They show a slight improvement with this approach on the video-to-gloss task.

Adaloglou et al. (2020) perform a comparative experimental assessment of computer vision-based methods for the video-to-gloss task. They implement various approaches from previous research (Camgöz et al. 2017; Cui, Liu, and Zhang 2019; Vaezi Joze and Koller 2019) and test them on multiple datasets (Huang et al. 2018; Cihan Camgöz et al. 2018; Von Agris and Kraiss 2007; Vaezi Joze and Koller 2019) either for isolated sign recognition or continuous sign recognition. They conclude that 3D convolutional models outperform models using only recurrent networks to capture the temporal information, and that these models are more scalable given the restricted receptive field, which results from the CNN “sliding window” technique.


Gloss-to-Video—also known as sign language production—is the task to produce a video that adequately represents a sequence of signs written as gloss.

As of 2020, there is no research discussing the direct translation task between a gloss to video. We believe this is a result of the computational impracticality of the desired model, which led researchers to avoid performing this task directly and instead rely on pipeline approaches using intermediate pose representations.


Gloss-to-Text—also known as sign language translation—is the natural language processing task of translating between gloss text representing sign language signs and spoken language text. These texts commonly differ by terminology, capitalization, and sentence structure.

Cihan Camgöz et al. (2018) experimented with various machine-translation architectures and compared between using an LSTM vs. GRU for the recurrent model, as well as Luong attention (Luong, Pham, and Manning 2015) vs. Bahdanau attention (Bahdanau, Cho, and Bengio 2015), and various batch-sizes. They concluded that on the RWTH-PHOENIX-Weather-2014T dataset, which was also presented by this work, using GRUs, Luong attention, and a batch size of 1 outperforms all other configurations.

In parallel with the advancements in spoken language machine translation, Yin and Read (2020) proposed replacing the RNN with a Transformer (Vaswani et al. 2017) encoder-decoder model, showing improvements on both RWTH-PHOENIX-Weather-2014T (DGS) and ASLG-PC12 (ASL) datasets both using a single model and ensemble of models. Interestingly, in gloss-to-text, they show that using the sign language recognition (video-to-gloss) system output outperforms using the gold annotated glosses.

Building on the code published by Yin and Read (2020), (???) TODO show it is beneficial to pre-train these translation models using augmented monolingual spoken language corpora. They try three different approaches for data augmentation: (1) Back-translation; (2) General text-to-gloss rules, including lemmatization, word reordering, and dropping of words; (3) Language pair specific rules that augment the spoken language syntax to its corresponding sign language syntax. When pretraining, all augmentations show improvements over the baseline for both RWTH-PHOENIX-Weather-2014T (DGS) and NCSLGR (ASL).


Text-to-gloss—also knows as sign language translation—is the task to translate between a spoken language text and sign language glosses.

Zhao et al. (2000) used a Tree Adjoining Grammar (TAG) based system for translating between English sentences and American Sign Language glosses. They parse the English text and simultaneously assemble an American Sign Language gloss tree, using Synchronous TAGs (Shieber and Schabes 1990; Shieber 1994), by associating the ASL elementary trees with the English elementary trees and associating the nodes at which subsequent substitutions or adjunctions can take place. Synchronous TAGs have been used for machine translation between spoken languages (Abeillé, Schabes, and Joshi 1991), but this is the first application to a signed language.

For the automatic translation of gloss-to-text, Othman and Jemni (2012) identified the need for a large parallel sign language gloss and spoken language text corpus. They develop a part-of-speech based grammar to transform English sentences taken from the Gutenberg Project ebooks collection (Lebert 2008) into American Sign Language gloss. Their final corpus contains over 100 million synthetic sentences and 800 million words and is the largest English-ASL gloss corpus that we know of. Unfortunately, it is hard to attest to the quality of the corpus, as they didn’t evaluate their method on real English-ASL gloss pairs, and only a small sample of this corpus is available online.


Video-to-text—also knows as sign language translation—is the entire task of translating a raw video to spoken language text.

Camgöz et al. (2020b) proposed a single architecture to perform this task that can use both the sign language gloss and the spoken language text in joint-supervision. They use the pre-trained spatial embeddings from Koller et al. (2019) to encode each frame independently and encode the frames with a transformer. On this encoding, they use a Connectionist Temporal Classification (CTC) (Graves et al. 2006) to classify the sign language gloss. Using the same encoding, they also use a transformer decoder to decode the spoken language text one token at a time. They show that adding gloss supervision improves the model over not using it and that it outperforms previous video-to-gloss-to-text pipeline approaches (Cihan Camgöz et al. 2018).

Following up, Camgöz et al. (2020a) propose a new architecture that does not require the supervision of glosses, called “Multi-channel Transformers for Multi-articulatory Sign Language Translation”. In this approach, they crop the signing hand and the face and perform 3D pose estimation to obtain three separate data channels. They encode each data channel separately using a transformer, then encode all channels together and concatenate the separate channels for each frame. Like their previous work, they use a transformer decoder to decode the spoken language text, but unlike their previous work, do not use the gloss as additional supervision. Instead, they add two “anchoring” losses to predict the hand shape and mouth shape from each frame independently, as silver annotations are available to them using the model proposed in Koller et al. (2019). They conclude that this approach is on-par with previous approaches requiring glosses, and so they have broken the dependency upon costly annotated gloss information in the video-to-text task.


Text-to-Video—also known as sign language production—is the task to produce a video that adequately represents a spoken language text in sign language.

As of 2020, there is no research discussing the direct translation task between text to video. We believe this is a result of the computational impracticality of the desired model, which led researchers to avoid performing this task directly and instead rely on pipeline approaches using intermediate pose representations.


Pose-to-text—also knows as sign language translation—is the task of translating a captured or estimated pose sequence to spoken language text.

Ko et al. (2019) demonstrate impressive performance on the pose-to-text task by inputting the pose sequence into a standard encoder-decoder translation network. They experiment both with GRU and various types of attention (Luong, Pham, and Manning 2015; Bahdanau, Cho, and Bengio 2015) and with a Transformer (Vaswani et al. 2017), and show similar performance, with the transformer underperforming on the validation set and overperforming on the test set, which consists of unseen signers. They experiment with various normalization scheme, mainly subtracting the mean and dividing by the standard deviation of every individual keypoint either with respect to the entire frame or to the relevant “object” (Body, Face, and Hand).


Text-to-Pose—also known as sign language production—is the task to produce a sequence of poses that adequately represent a spoken language text in sign language.

Saunders, Camgöz, and Bowden (2020c) propose Progressive Transformers, a model to translate from discrete spoken language sentences to continuous 3D sign pose sequences in an autoregressive manner. Unlike symbolic transformers (Vaswani et al. 2017), which use a discrete vocabulary and thus can predict an end-of-sequence (EOS) token in every step, the progressive transformer predicts a counter ∈ [0, 1] in addition to the pose. In inference time, counter = 1 is considered as the end of the sequence. They test their approach on the RWTH-PHOENIX-Weather-2014T dataset using OpenPose 2D pose estimation, uplifted to 3D (Zelinka and Kanis 2020), and show favorable results when evaluating using back-translation from the generated poses to spoken language. They further show (Saunders, Camgöz, and Bowden 2020a) that using an adversarial discriminator between the ground truth poses, and the generated poses, conditioned on the input spoken language text improves the production quality as measured using back-translation.

To overcome the issues of under-articulation seen in the above works, Saunders, Camgöz, and Bowden (2020b) expands on the progressive transformer model using a Mixture Density Network (MDN) (Bishop 1994) to model the variation found in sign language. While this model underperforms on the validation set, compared to previous work, it outperforms on the test set.

Zelinka and Kanis (2020) present a similar autoregressive decoder approach, with added dynamic-time-warping (DTW) and soft attention. They test their approach on Czech Sign Language weather data extracted from the news, which is not manually annotated, or aligned to the spoken language captions, and show their DTW is advantageous for this kind of task.

Xiao, Qin, and Yin (2020) close the loop by proposing a text-to-pose-to-text model for the case of isolated sign language recognition. They first train a classifier to take a sequence of poses encoded by a BiLSTM and classify the relevant sign, then, propose a production system to take a single sign and sample a constant length sequence of 50 poses from a Gaussian Mixture Model. These components are combined such that given a sign class y, a pose sequence is generated, then classified back into a sign class ŷ, and the loss is applied between y and ŷ, and not directly on the generated pose sequence. They evaluate their approach on the CSL dataset (Huang et al. 2018) and show that their generated pose sequences almost reach the same classification performance as the real sequences.


As of 2020, there is no research discussing the translation task between a writing notation system to any other modality.


As of 2020, there is no research discussing the translation task between any modality to a writing notation system.


Fingerspelling is the act of spelling a word letter-by-letter, borrowing from the spoken language alphabet (Battison 1978; Wilcox 1992; Brentari and Padden 2001). This phenomenon, found in most signed languages, often occurs when there is no previously agreed upon sign for a concept, like in technical language, colloquial conversations involving names, conversations involving current events, emphatic forms, and the context of code-switching between the sign language and corresponding spoken language (Padden 1998; Montemurro and Brentari 2018). The relative amount of fingerspelling varies between signed languages, and for American Sign Language (ASL) accounts for 12–35% of the signed content (Padden and Gunsauls 2003).

Patrie and Johnson (2011) describe the following terminology to describe three different forms of fingerspelling:


Fingerspelling recognition–a sub-task of sign language recognition–is the task to recognize fingerspelled words from a sign language video.

Shi et al. (2018) introduced a large dataset available for American Sign Language fingerspelling recognition. This dataset includes both the “careful” and “rapid” forms of fingerspelling collected from naturally occurring videos “in the wild”, which are more challenging than studio conditions. They train a baseline model to take a sequence of images cropped around the signing hand and either use an autoregressive decoder or a CTC. They found that the CTC outperforms the autoregressive decoder model, but both achieve a very low recognition rate (35-41% character level accuracy) compared to human performance (around 82%).

In follow-up work, Shi et al. (2019) collected nearly an order-of-magnitude larger dataset and designed a new recognition model. Instead of detecting the signing hand, they detect the face and crop a large area around it. Then, they perform an iterative process of zooming in to the hand using visual attention in order to retain sufficient information in high resolution of the hand. Finally, like their previous work, they encode the image hand crops sequence and use a CTC to obtain the frame labels. They show that this method outperforms their original “hand crop” method by 4%, and that using the additional data collected, they can achieve up to 62.3% character level accuracy. Looking through this dataset, we note that the videos in the dataset are taken from longer videos, and as they are cut, they do not retain the signing before the fingerspelling. This context relates to language modeling, where at first one fingerspells a word carefully, and when repeating it, might fingerspell it rapidly, but the interlocutors can infer they are fingerspelling the same word.


Fingerspelling production–a sub-task of sign language production–is the task of producing a fingerspelling video for words.

In its most basic form, “Careful” fingerspelling production can be trivially solved using pre-defined letter handshapes interpolation. Adeline (2013) demonstrates this approach for American Sign Language and English fingerspelling. They rig a hand armature for each letter in the English alphabet (N = 26) and generate all (N2 = 676) transitions between every two letters using interpolation or manual animation. Then, to fingerspell full words, they chain pairs of letter transitions. For example, for the word “CHLOE”, they would chain the following transitions sequentially: #C CH HL LO OE E#.

However, to produce life-like animations, one must also consider the rhythm and speed of holding letters, and transitioning between letters, as those can affect how intelligible fingerspelling motions are to an interlocutor (Wilcox (1992)). Wheatland et al. (2016) analyzes both “careful” and “rapid” fingerspelling videos for these features. They find that for both forms of fingerspelling, on average, the longer the word, the shorter the transition and hold time. Furthermore, they find that less time is spent on middle letters on average, and the last letter is held on average longer than the other letters in the word. Finally, they use this information to construct an animation system using letter pose interpolation and control the timing using a data-driven statistical model.

Annotation Tools

ELAN - EUDICO Linguistic Annotator

ELAN (Wittenburg et al. 2006) is an annotation tool for audio and video recordings. With ELAN, a user can add an unlimited number of textual annotations to audio and/or video recordings. An annotation can be a sentence, word, gloss, comment, translation, or a description of any feature observed in the media. Annotations can be created on multiple layers, called tiers, which can be hierarchically interconnected. An annotation can either be time-aligned to the media or refer to other existing annotations. The content of annotations consists of Unicode text, and annotation documents are stored in an XML format (EAF). ELAN is open source (GPLv3), and installation is available for Windows, macOS, and Linux. PyMPI (Lubbers and Torreira 2013) allows for simple python interaction with Elan files.


iLex (Hanke 2002) is a tool for sign language lexicography and corpus analysis, that combines features found in empirical sign language lexicography and in sign language discourse transcription. It supports the user in integrated lexicon building while working on the transcription of a corpus and offers a number of unique features considered essential due to the specific nature of signed languages. iLex binaries are available for macOS.


SignStream (Neidle, Sclaroff, and Athitsos 2001) is a tool for linguistic annotations and computer vision research on visual-gestural language data SignStream installation is only available for old versions of MacOS and is distributed under MIT license.

Anvil - The Video Annotation Research Tool

Anvil (Kipp 2001) is a free video annotation tool, offering multi-layered annotation based on a user-defined coding scheme. In Anvil, the annotator can see color-coded elements on multiple tracks in time-alignment. Some special features are cross-level links, non-temporal objects, timepoint tracks, coding agreement analysis, 3D viewing of motion capture data and a project tool for managing whole corpora of annotation files. Anvil installation is available for Windows, macOS, and Linux.

Existing Datasets

Currently, there is no easy way or agreed upon format to download and load sign language datasets, and as such, evaluation on these datasets is scarce. As part of this work, we streamlined the loading of available datasets using Tensorflow Datasets (“TensorFlow Datasets, a Collection of Ready-to-Use Datasets,” n.d.). This allows researchers to load large and small datasets alike with a simple command and be comparable to other works. We make these datasets available using a custom library, Sign Language Datasets.

import tensorflow_datasets as tfds
import sign_language_datasets.datasets

# Loading a dataset with default configuration
aslg_pc12 = tfds.load("aslg_pc12")

# Loading a dataset with custom configuration
from sign_language_datasets.datasets.config import SignDatasetConfig
config = SignDatasetConfig(name="videos_and_poses256x256:12", 
                           version="3.0.0",          # Specific version
                           include_video=True,       # Download and load dataset videos
                           fps=12,                   # Load videos at constant, 12 fps
                           resolution=(256, 256),    # Convert videos to a constant resolution, 256x256
                           include_pose="holistic")  # Download and load Holistic pose estimation
rwth_phoenix2014_t = tfds.load(name='rwth_phoenix2014_t', builder_kwargs=dict(config=config))

Furthermore, we follow a unified interface when possibles, making attributes the same and comparable between datasets:

    "id": tfds.features.Text(),
    "signer": tfds.features.Text() | tf.int32,
    "video": tfds.features.Video(shape=(None, HEIGHT, WIDTH, 3)),
    "depth_video": tfds.features.Video(shape=(None, HEIGHT, WIDTH, 1)),
    "fps": tf.int32,
    "pose": {
        "data": tfds.features.Tensor(shape=(None, 1, POINTS, CHANNELS), dtype=tf.float32),
        "conf": tfds.features.Tensor(shape=(None, 1, POINTS), dtype=tf.float32)
    "gloss": tfds.features.Text(),
    "text": tfds.features.Text()

The following table contains a curated list of datasets including various signed languages and data formats:

🎥 Video | 👋 Pose | 👄 Mouthing | ✍ Notation | 📋 Gloss | 📜 Text | 🔊 Speech

Dataset Publication Language Features #Signs #Samples #Signers License
ASL-100-RGBD Hassan et al. (2020) American 🎥👋📋 100 4,150 Tokens 22 Authorized Academics
ASLG-PC12 💾 Othman and Jemni (2012) American (Synthetic) 📋📜 > 100,000,000 Sentences N/A Sample Available (1, 2)
ASLLVD Athitsos et al. (2008) American TODO 3,000 12,000 Samples 4 Attribution
ATIS Bungeroth et al. (2008) Multilingual TODO 292 595 Sentences
AUSLAN Johnston (2010) Australian TODO 1,100 Videos 100
AUTSL 💾 Sincan and Keles (2020) Turkish 🎥📋 226 36,302 Samples 43 Codalab
BosphorusSign Camgöz et al. (2016) Turkish TODO 636 24,161 Samples 6 Not Published
BSL Corpus Schembri et al. (2013) British TODO 40,000 Lexical Items 249 Partially Restricted
CDPSL Łacheta and Rutkowski (2014) Polish 🎥✍📜 300 hours
ChicagoFSWild Shi et al. (2018) American 🎥📜 26 7,304 Sequences 160 Public
ChicagoFSWild+ Shi et al. (2019) American 🎥📜 26 55,232 Sequences 260 Public
CopyCat Zafrulla et al. (2010) American TODO 22 420 Phrases 5
Corpus NGT 💾 Crasborn and Zwitserlood (2008) Netherlands TODO 15 Hours 92 CC BY-NC-SA 3.0 NL
DEVISIGN Chai, Wang, and Chen (2014) Chinese TODO 2,000 24,000 Samples 8 Research purpose on request
Dicta-Sign Matthes et al. (2012) Multilingual TODO 6-8 Hours (/Participant) 16-18 /Language
How2Sign 💾 Duarte et al. (2020) American 🎥👋📋📜🔊 16,000 79 hours (35,000 sentences) 11 CC BY-NC 4.0
K-RSL Imashev et al. (2020) Kazakh-Russian 🎥👋📜 600 28,250 Videos 10 Attribution
KETI Ko et al. (2019) Korean 🎥👋📋📜 524 14,672 Videos 14 TODO (emailed Sang-Ki Ko)
LSE-SIGN Gutierrez-Sigut et al. (2016) Spanish TODO 2,400 2,400 Samples 2
MS-ASL Vaezi Joze and Koller (2019) American TODO 1,000 25,000 (25 hours) 200 Public
NCSLGR 💾 Databases (2007) American 🎥📋📜 1,875 sentences 4 TODO
Public DGS Corpus Hanke et al. (2020) German 🎥🎥👋👄✍📋📜📜 50 Hours 330 Custom
RVL-SLLL ASL Martínez et al. (2002) American TODO 104 2,576 Videos 14 Research Attribution
RWTH Fingerspelling Dreuw et al. (2006) German 🎥📜 35 1,400 single-char videos 20
RWTH-BOSTON-104 Dreuw et al. (2008) American 🎥📜 104 201 Sentences 3
RWTH-PHOENIX-Weather T 💾 Forster et al. (2014);Cihan Camgöz et al. (2018) German 🎥📋📜 1,231 8,257 Sentences 9 CC BY-NC-SA 3.0
S-pot Viitaniemi et al. (2014) Finnish TODO 1,211 5,539 Videos 5 Permission
Sign2MINT 💾 2021 German 🎥✍📜 740 1135 CC BY-NC-SA 3.0 DE
SIGNOR Vintar, Jerko, and Kulovec (2012) Slovene 🎥👄✍📋📜 80 TODO emailed Špela
SIGNUM Von Agris and Kraiss (2007) German TODO 450 15,600 Sequences 20
SMILE Ebling et al. (2018) Swiss-German TODO 100 9,000 Samples 30 Not Published
SSL Corpus Öqvist, Riemer Kankkonen, and Mesch (2020) Swedish 🎥✍📋📜 TODO In January
SSL Lexicon Mesch and Wallin (2012) Swedish 🎥📋📜📜 20,000 CC BY-NC-SA 2.5 SE
Video-Based CSL Huang et al. (2018) Chinese TODO 500 125,000 Videos 50 Research Attribution
WLASL 💾 Li et al. (2020) American 🎥📋 2,000 100 C-UDA 1.0


For attribution in academic contexts, please cite this work as:

    title = "{S}ign {L}anguage {P}rocessing", 
    author = "Moryossef, Amit and Goldberg, Yoav",
    howpublished = "\url{}",
    year = "2021"


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