# A Comparative Analysis of Techniques and Algorithms for Recognising Sign Language

**Authors:** Rupesh Kumar, Ayush Sinha, Ashutosh Bajpai, S. K Singh  
**arXiv:** [2305.13941](https://arxiv.org/abs/2305.13941)  
**Date:** 2023-05-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.13941v2/

## Key points

- Sign language is a visual language used by people with hearing loss
- Many individuals with hearing loss do not use sign language and experience social isolation
- Developing human-computer interface systems for the hearing-impaired community is important
- Current commercial sign language translation systems are sensor-based, expensive, and difficult to use
- Vision-based systems face challenges that need to be overcome
- Previous techniques for continuous sign language recognition have limitations in capturing temporal information
- Machine learning approaches are being applied to convert hand and sign language motions into spoken or written language
- This study compares different deep learning techniques for recognizing sign language
- The survey provides an overview of recent approaches and challenges in sign language recognition
- Techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks are discussed
- Feature extraction methods are explored and algorithm performance is evaluated for sign language recognition
- Accurate and efficient SLR systems can enhance communication for individuals with hearing loss
- The research contributes to the advancement of human-computer interaction technology

## Summary

Sign language is a visual language that is commonly used by people with hearing loss as their primary form of communication. However, many individuals with hearing loss do not use sign language and often experience social isolation as a result. To address this issue, it is important to develop human-computer interface systems that can provide a social platform for the hearing-impaired community. Currently available commercial sign language translation systems are sensor-based, expensive, and difficult to use. While vision-based systems are needed, they face several challenges that need to be overcome. Previous techniques for continuous sign language recognition have used hidden Markov models (HMM), which have limitations in capturing temporal information. To overcome these limitations, various machine learning approaches are being applied to convert hand and sign language motions into spoken or written language. This study focuses on comparing different deep learning techniques for recognizing sign language. The survey conducted aims to provide a comprehensive overview of the most recent approaches and challenges in the field of sign language recognition. The authors analyze recent developments in deep learning-based sign language recognition (SLR) and discuss techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. The study also explores feature extraction methods and evaluates the performance of different algorithms for SLR. The authors emphasize the importance of developing accurate and efficient SLR systems that can enhance communication for individuals with hearing loss. Overall, this research contributes to the advancement of human-computer interaction technology by providing insights into the current state of sign language recognition techniques and identifying areas for further improvement.