Bangla sign digits recognition using depth information

S. M. Rayeed; Gazi Wasif Akram; Sidratul Tamzida Tuba; Golam Sadman Zilani; Hasan Mahmud; Md. Kamrul Hasan

doi:10.1117/12.2623400

4 March 2022 Bangla sign digits recognition using depth information

S. M. Rayeed, Gazi Wasif Akram, Sidratul Tamzida Tuba, Golam Sadman Zilani, Hasan Mahmud, Md. Kamrul Hasan

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120840P (2022) https://doi.org/10.1117/12.2623400
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. The task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing dataset for Sign Language Recognition (SLR) in Bangla Sign Language (BdSL) is based on RGB images. Recent research on sign language recognition has shown better recognition accuracy using depth-based features. In this paper, we present a complete dataset for Bangla sign digits from Zero (Shunno in Bangla) to Nine (Noy in Bangla) using MediaPipe, a cross-platform depth-map estimation framework. The proposed method can utilize hand skeleton joint points containing depth information in addition to x, y coordinates from RGB images only. To validate the effectiveness of our proposed approach, we have run MediaPipe on a benchmark American Sign Language (ASL) dataset. Running different classifiers in our proposed dataset we got 98.65% using Support Vector Machine (SVM). Moreover, we compared our dataset with the existing Bangla digit dataset Ishara Bochon using deep learning based approach and achieved significantly higher accuracy.

Citation Download Citation

S. M. Rayeed, Gazi Wasif Akram, Sidratul Tamzida Tuba, Golam Sadman Zilani, Hasan Mahmud, and Md. Kamrul Hasan "Bangla sign digits recognition using depth information", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120840P (4 March 2022); https://doi.org/10.1117/12.2623400

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KEYWORDS

RGB color model

3D modeling

Data modeling

Gesture recognition

Cameras

Image classification

Image segmentation

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