# Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map

arXiv: https://arxiv.org/abs/1509.03001 · HTML: https://mlsanity.com/paper/1509.03001/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2015.

## Built on by (most cited first, 6 of 6)

- [Survey on Hand Gesture Recognition from Visual Input](https://mlsanity.com/paper/2501.11992/) (2025)
- [Enhancing ASL Recognition with GCNs and Successive Residual Connections](https://mlsanity.com/paper/2408.09567/) (2024)
- [HandReader: Advanced Techniques for Efficient Fingerspelling Recognition](https://mlsanity.com/paper/2505.10267/) (2025)
- [BdSpell: A YOLO-based Real-time Finger Spelling System for Bangla Sign Language](https://mlsanity.com/paper/2309.13676/) (2023)
- [Enhancing Sign Language Detection through Mediapipe and Convolutional Neural Networks (CNN)](https://mlsanity.com/paper/2406.03729/) (2024)
- [Bukva: Russian Sign Language Alphabet](https://mlsanity.com/paper/2410.08675/) (2024)

## Abstract (arXiv)

Sign language recognition is important for natural and convenient communication between deaf community and hearing majority. We take the highly efficient initial step of automatic fingerspelling recognition system using convolutional neural networks (CNNs) from depth maps. In this work, we consider relatively larger number of classes compared with the previous literature. We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects. While using different learning configurations, such as hyper-parameter selection with and without validation, we achieve 99.99% accuracy for observed signers and 83.58% to 85.49% accuracy for new signers. The result shows that accuracy improves as we include more data from different subjects during training. The processing time is 3 ms for the prediction of a single image. To the best of our knowledge, the system achieves the highest accuracy and speed. The trained model and dataset is available on our repository.