ARCHIVES
Original Article
Sign Language Detection in Video and Real-Time Video Calls
VM Saravana Perumal1
Manjunath Suresh Patil2
Nikhil S H3
Mallinath4
Rajath K N5
1 2 3 4 5 Department. of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru, Karnataka, India.
Published Online: November-December 2025
Pages: 195-199
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250506025
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.
Related Articles
2025
Exploring Mathematical Concepts in Ramcharit Manas: A Unique Perspective on Navadha Bhakti
2025
ARMOIRE An Augmented Reality Fashion Try On
2025
Sign Vision AI powered sign language Recognition
2025
Drowzy Alert AI Powered Driver Fatigue Detection
2025
Beauty Care Shopping using 3D Modelling
2025
