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Sign Language Detection in Video and Real-Time Video Calls
Authors
1 2 3 4 5 Department. of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru, Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 195-199
- Article Type
- Original Article
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References
1. J. Sharma, R. Singh and P. Kumar, “Indian Sign Language Recogni- tion using Deep Learning Approaches,” *IEEE Access*, vol. 7, pp. 12312–12320, 2019.
2. S. Kumar and A. Roy, “Static Hand Gesture Recognition Using Convo- lutional Neural Networks,” *Procedia Computer Science*, vol. 167, pp. 1234–1245, 2021.
3. F. Chollet, “Deep Learning with Python,” Manning Publications, 2018.
4. A. Graves, “Long Short-Term Memory,” *Neural Computation*, vol. 9, no. 8, pp. 1735–1780, 1997.
5. G. Hinton et al., “Deep Neural Networks for Acoustic Modeling,” *IEEE Signal Processing Magazine*, vol. 29, no. 6, pp. 82–97, Nov. 2012.
6. Z. Nagi et al., “Real-Time Hand Gesture Recognition Using Vision- Based Techniques,” *IEEE Transactions on Multimedia*, vol. 19, no. 12, pp. 2790–2799, 2017.
7. M. Kim and H. Byun, “Hand Gesture Recognition Using 3D CNNs for Real-Time Human–Computer Interaction,” *IEEE Access*, vol. 8, pp. 14532–14541, 2020.
8. Y. Zhang, C. Zhang and M. Wang, “Dynamic Hand Gesture Recog- nition Based on LSTM Networks,” *IEEE Transactions on Industrial Informatics*, vol. 16, no. 1, pp. 188–197, Jan. 2020.
9. Google Research, “MediaPipe: Cross-platform Machine Learning Pipelines,” https://mediapipe.dev, 2020.
10. D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” *International Conference on Learning Representations (ICLR)*, 2015.
11. S. Sridhar and A. Lu, “Real-Time Vision-Based Gesture Recognition Using Deep Learning,” *IEEE Transactions on Image Processing*, vol. 29, pp. 255–268, 2020.
12. T. Starner, “Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video,” *IEEE Transactions on Pat- tern Analysis and Machine Intelligence*, vol. 20, no. 12, pp. 1371–1375, 1998.
13. D. Kelly et al., “A Person-Independent System for Recognizing Hand Gestures,” *Pattern Recognition Letters*, vol. 31, pp. 1624–1632, 2010.
14. W. Li, Z. Zhang and Z. Liu, “Action Recognition Based on a Bag of 3D Points,” *IEEE CVPR Workshops*, pp. 9–14, 2010.
2. S. Kumar and A. Roy, “Static Hand Gesture Recognition Using Convo- lutional Neural Networks,” *Procedia Computer Science*, vol. 167, pp. 1234–1245, 2021.
3. F. Chollet, “Deep Learning with Python,” Manning Publications, 2018.
4. A. Graves, “Long Short-Term Memory,” *Neural Computation*, vol. 9, no. 8, pp. 1735–1780, 1997.
5. G. Hinton et al., “Deep Neural Networks for Acoustic Modeling,” *IEEE Signal Processing Magazine*, vol. 29, no. 6, pp. 82–97, Nov. 2012.
6. Z. Nagi et al., “Real-Time Hand Gesture Recognition Using Vision- Based Techniques,” *IEEE Transactions on Multimedia*, vol. 19, no. 12, pp. 2790–2799, 2017.
7. M. Kim and H. Byun, “Hand Gesture Recognition Using 3D CNNs for Real-Time Human–Computer Interaction,” *IEEE Access*, vol. 8, pp. 14532–14541, 2020.
8. Y. Zhang, C. Zhang and M. Wang, “Dynamic Hand Gesture Recog- nition Based on LSTM Networks,” *IEEE Transactions on Industrial Informatics*, vol. 16, no. 1, pp. 188–197, Jan. 2020.
9. Google Research, “MediaPipe: Cross-platform Machine Learning Pipelines,” https://mediapipe.dev, 2020.
10. D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” *International Conference on Learning Representations (ICLR)*, 2015.
11. S. Sridhar and A. Lu, “Real-Time Vision-Based Gesture Recognition Using Deep Learning,” *IEEE Transactions on Image Processing*, vol. 29, pp. 255–268, 2020.
12. T. Starner, “Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video,” *IEEE Transactions on Pat- tern Analysis and Machine Intelligence*, vol. 20, no. 12, pp. 1371–1375, 1998.
13. D. Kelly et al., “A Person-Independent System for Recognizing Hand Gestures,” *Pattern Recognition Letters*, vol. 31, pp. 1624–1632, 2010.
14. W. Li, Z. Zhang and Z. Liu, “Action Recognition Based on a Bag of 3D Points,” *IEEE CVPR Workshops*, pp. 9–14, 2010.
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