# Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique

arXiv: https://arxiv.org/abs/1303.0634 · HTML: https://mlsanity.com/paper/1303.0634/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2013.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets](https://mlsanity.com/paper/2204.03328/) (2022)
- [BAUST Lipi: A BdSL Dataset with Deep Learning Based Bangla Sign Language Recognition](https://mlsanity.com/paper/2408.10518/) (2024)
- [Bangla sign language recognition using concatenated BdSL network](https://mlsanity.com/paper/2107.11818/) (2021)
- [Sign Language Recognition System using TensorFlow Object Detection API](https://mlsanity.com/paper/2201.01486/) (2022)

## Abstract (arXiv)

Sign Language Recognition is one of the most growing fields of research today. Many new techniques have been developed recently in these fields. Here in this paper, we have proposed a system using Eigen value weighted Euclidean distance as a classification technique for recognition of various Sign Languages of India. The system comprises of four parts: Skin Filtering, Hand Cropping, Feature Extraction and Classification. Twenty four signs were considered in this paper, each having ten samples, thus a total of two hundred forty images was considered for which recognition rate obtained was 97 percent.