# Isolated Sign Language Recognition based on Tree Structure Skeleton Images arXiv: https://arxiv.org/abs/2304.05403 ยท HTML: https://mlsanity.com/paper/2304.05403/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 33 references that are in the map. - First posted on arXiv 10 Apr 2023. ## Builds on (most cited first, 18 of 18) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018) - [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018) - [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019) - [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021) - [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018) - [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019) - [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (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) - [Transferring Cross-domain Knowledge for Video Sign Language Recognition](https://mlsanity.com/paper/2003.03703/) (2020) - [Pose-based Sign Language Recognition using GCN and BERT](https://mlsanity.com/paper/2012.00781/) (2020) - [Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition](https://mlsanity.com/paper/1901.11164/) (2019) - [Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition](https://mlsanity.com/paper/2110.12396/) (2021) - [ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research](https://mlsanity.com/paper/2105.05066/) (2021) - [Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1812.10550/) (2018) ## Built on by (most cited first, 15 of 15) - [MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition](https://mlsanity.com/paper/2405.20666/) (2024) - [Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition](https://mlsanity.com/paper/2406.10501/) (2024) - [Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition](https://mlsanity.com/paper/2506.03615/) (2025) - [Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment](https://mlsanity.com/paper/2512.08040/) (2025) - [SEDS: Semantically Enhanced Dual-Stream Encoder for Sign Language Retrieval](https://mlsanity.com/paper/2407.16394/) (2024) - [Enhancing Brazilian Sign Language Recognition through Skeleton Image Representation](https://mlsanity.com/paper/2404.19148/) (2024) - [Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution](https://mlsanity.com/paper/2407.21525/) (2024) - [Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks](https://mlsanity.com/paper/2506.04367/) (2025) - [Proper Body Landmark Subset Enables More Accurate and 5X Faster Recognition of Isolated Signs in LIBRAS](https://mlsanity.com/paper/2510.24887/) (2025) - [SignAgent: Agentic LLMs for Linguistically-Grounded Sign Language Annotation and Dataset Curation](https://mlsanity.com/paper/2603.19059/) (2026) - [Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition](https://mlsanity.com/paper/2505.02304/) (2025) - [Sign language recognition from skeletal data using graph and recurrent neural networks](https://mlsanity.com/paper/2511.05772/) (2025) - [The Influence of Iconicity in Transfer Learning for Sign Language Recognition](https://mlsanity.com/paper/2603.03316/) (2026) - [Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards](https://mlsanity.com/paper/2606.19352/) (2026) - [TransSLR: A Lightweight Transformer for Sign Language Recognition](https://mlsanity.com/paper/2608.06407/) (2026) ## Abstract (arXiv) Sign Language Recognition (SLR) systems aim to be embedded in video stream platforms to recognize the sign performed in front of a camera. SLR research has taken advantage of recent advances in pose estimation models to use skeleton sequences estimated from videos instead of RGB information to predict signs. This approach can make HAR-related tasks less complex and more robust to diverse backgrounds, lightning conditions, and physical appearances. In this work, we explore the use of a spatio-temporal skeleton representation such as Tree Structure Skeleton Image (TSSI) as an alternative input to improve the accuracy of skeleton-based models for SLR. TSSI converts a skeleton sequence into an RGB image where the columns represent the joints of the skeleton in a depth-first tree traversal order, the rows represent the temporal evolution of the joints, and the three channels represent the (x, y, z) coordinates of the joints. We trained a DenseNet-121 using this type of input and compared it with other skeleton-based deep learning methods using a large-scale American Sign Language (ASL) dataset, WLASL. Our model (SL-TSSI-DenseNet) overcomes the state-of-the-art of other skeleton-based models. Moreover, when including data augmentation our proposal achieves better results than both skeleton-based and RGB-based models. We evaluated the effectiveness of our model on the Ankara University Turkish Sign Language (TSL) dataset, AUTSL, and a Mexican Sign Language (LSM) dataset. On the AUTSL dataset, the model achieves similar results to the state-of-the-art of other skeleton-based models. On the LSM dataset, the model achieves higher results than the baseline. Code has been made available at: https://github.com/davidlainesv/SL-TSSI-DenseNet.