# ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research

arXiv: https://arxiv.org/abs/2105.05066 · HTML: https://mlsanity.com/paper/2105.05066/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 15 of 15)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Whole-Body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2007.11858/) (2020)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [Quantitative Survey of the State of the Art in Sign Language Recognition](https://mlsanity.com/paper/2008.09918/) (2020)
- [A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition](https://mlsanity.com/paper/2007.12530/) (2020)
- [Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition](https://mlsanity.com/paper/2006.11183/) (2020)

## Built on by (most cited first, 10 of 10)

- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Isolated Sign Language Recognition based on Tree Structure Skeleton Images](https://mlsanity.com/paper/2304.05403/) (2023)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition](https://mlsanity.com/paper/2110.12396/) (2021)
- [BdSLW60: A Word-Level Bangla Sign Language Dataset](https://mlsanity.com/paper/2402.08635/) (2024)
- [Towards the extraction of robust sign embeddings for low resource sign language recognition](https://mlsanity.com/paper/2306.17558/) (2023)
- [TSLFormer: A Lightweight Transformer Model for Turkish Sign Language Recognition Using Skeletal Landmarks](https://mlsanity.com/paper/2505.07890/) (2025)
- [Academic competitions](https://mlsanity.com/paper/2312.00268/) (2023)
- [Sign language recognition based on deep learning and low-cost handcrafted descriptors](https://mlsanity.com/paper/2408.07244/) (2024)
- [FlashSign: Pose-Free Guidance for Efficient Sign Language Video Generation](https://mlsanity.com/paper/2603.27915/) (2026)

## Abstract (arXiv)

The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories.