# American Sign Language to Text Translation using Transformer and Seq2Seq with LSTM

arXiv: https://arxiv.org/abs/2409.10874 · HTML: https://mlsanity.com/paper/2409.10874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [TENER: Adapting Transformer Encoder for Named Entity Recognition](https://mlsanity.com/paper/1911.04474/) (2019)
- [Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition](https://mlsanity.com/paper/1701.03360/) (2017)
- [CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition](https://mlsanity.com/paper/1904.02141/) (2019)

## Abstract (arXiv)

Sign language translation is one of the important issues in communication between deaf and hearing people, as it expresses words through hand, body, and mouth movements. American Sign Language is one of the sign languages used, one of which is the alphabetic sign. The development of neural machine translation technology is moving towards sign language translation. Transformer became the state-of-the-art in natural language processing. This study compares the Transformer with the Sequence-to-Sequence (Seq2Seq) model in translating sign language to text. In addition, an experiment was conducted by adding Residual Long Short-Term Memory (ResidualLSTM) in the Transformer. The addition of ResidualLSTM to the Transformer reduces the performance of the Transformer model by 23.37% based on the BLEU Score value. In comparison, the Transformer itself increases the BLEU Score value by 28.14 compared to the Seq2Seq model.