Language has always played a significant role in Human to Human communication. In case of not knowing someone else’s language, one can use hand gestures for communicating crudely but still be able to convey the message. Other than not knowing someone else’s language there are millions of people in the world who have hearing or speaking disability. According to the World Health Organization (WHO), it has been estimated to be a population of 436 million (5% of the total world’s population) people in the world who have hearing disabilities. Deaf people cannot use oral languages for communicating with other people. The source of their communication is Sign Language (SL) that conveys the message to the other person. In Computer Vision, there are different algorithms, which are used to interpret gestures and recognize them. The Deaf community of Pakistan uses its SL, like any other country in the world i.e., Pakistan Sign Language (PSL). There are around 60 local languages that are spoken in Pakistan including Hindko. Hindko and many other local languages spoken by minorities in Pakistan are on the brink of being endangered as the amount of research done on these languages is almost negligible. In this paper Convolutional Neural Network (CNN) is used for the recognition of Hindko Sign Language (HSL). Furthermore, we examine and analyze the recognition based on prediction to evaluate the efficiency of the utilized CNN. The methodology developed in this research work achieved an accuracy rate of 99.98%.
Ali Raza Dr.Syed Irfan Ullah "Hindko Sign Language (HSL) Recognition Using Convolutional Neural Network" International Journal Vol. 7 Issue 09 PP. 305-311 September 2020 https://doi.org/10.34259/ijew.20.709305311.
 Verma, S., Omanwar, R., Sreejith V., and Meera, G. S., “A Smartphone Based Indoor Navigation System”, 28th IEEE International Conference on Microelectronics, pp. 345-348, 2016.
 Varga, R., and Prekopcsak, Z., “Creating a Database for Objective Comparison of Gesture Recognition System”, 15th International Student Conference on Electrical Engineering, pp. 1-6, 2011.
 Sigalas, M., Haris, B., and Panos, T., Gesture Recognition Based on Arm Tracking for Human-Robot Interaction, IEEE International Conference on Intelligent Robots and Systems, pp. 5424-5429, 2010.
 Lewis, M. P., Simons, G. F., and Fennig, C. D., “Ethnologue: Languages of The World”, Texas: SIL International, 2015. [Online]. Available:
 Sulman, D. N., and Zuberi, S., “Pakistan Sign Language–A Synopsis”, Pakistan, June, 2000. [Online]. Available: https://www.academia.edu/2708088/Pakistan_Sign_Language_-_A_Synopsis
 Organization, W. H., “10 Facts About Deafness”, Posjeceno, Vol. 14, pp. 2017, 2017.
 Toker, H., “A Practical Guide to Hindko Grammar”, Trafford Publishing, 2014. [Online]. Available:
 Han, J., Zhang, D., Cheng, G., Liu, N. and Xu, D., “Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey”, IEEE Signal Process. Mag., Vol. 35, No. 1, pp. 84–100, 2018.
 Lee, A., “Comparing Deep Neural Networks and Traditional Vision Algorithms in Mobile Robotics,” Traditional Vision Algorithms in Mobile Robotics”, 2016.
 Krizhevsky, A., Sutskever, I., and Hinton, G., “Image Net Classification with Deep Convolutional Neural Networks”, In Proceedings of The Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, pp. 1097–1105, 2012.
 Chaman, S., Dsouza, D., Dmello, B., Bhavsar, K., and Dsouza, T., “Real-Time Hand Gesture Communication System in Hindi for Speech and Hearing Impaired”, IEEE International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1954-1958, 2018.
 Islam, M. R., Mitu, U.K., Bhuiyan R. A., and Shin, J., “Hand Gesture Feature Extraction Using Deep Convolutional Neural Network for Recognizing American Sign Language”, IEEE 4th International Conference on Frontiers of Signal Processing (ICFSP), pp. 115-119, 2018.
 Sun, J. H., Ji, T. T., Zhang, S. B., Yang, J. K., and Ji, G. R., “Research on the Hand Gesture Recognition Based on Deep Learning”, 12th IEEE International Symposium on Antennas, Propagation and EM Theory (ISAPE), pp. 1-4, December, 2018.
 Khan, N., Shahzada, A., Ata, S., Abid, A., Khan Y., and Farooq, M.S., “A Vision-Based Approach for Pakistan Sign Language Alphabets Recognition”, Pensee, Vol. 76, No. 3, pp. 274-285, 2014.
 Dadiz, B.G., Abrasia, J. M. B., and Jimenez, J. L., “Go-Mo (Go-Motion): An Android Mobile Application Detecting Motion Gestures for Generating Basic Mobile Phone Commands Utilizing KLT Algorithm”, IEEE 2nd International Conference on Signal and Image Processing (ICSIP), pp. 30-34, 2017.
 Malik, M. S. A., Kousar, N., Abdullah, T., Ahmed, M., Rasheed, F., and Awais, M., “Pakistan Sign Language Detection using PCA and KNN”, International Journal of Advanced Computer Science and Applications, Vol. 9, No. 54, pp. 78-81, 2018.
 Chong T.W., and Lee, B.G., “American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach”, Sensors, Vol. 18, No. 10, pp. 3554, 2018.
 Thongtawee, A., Onamon, P., and Yuttana, K., A Novel Feature Extraction for American Sign Language Recognition Using Webcam, 11th IEEE Biomedical Engineering International Conference (BMEiCON), pp. 1-5, 2018.
 Shah, S. M. S., Naqvi, H. A., Khan, J. I., Ramzan, M., and Khan, H. U., Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines, IEEE Access, Vol. 6, pp. 59242-59252, 2018.
 Goodfellow, I., Bengio Y., and Courville, A., “Deep Learning”, MIT Press: Cambridge, MA, USA, 2016. [Online]. Available
 Krizhevsky, A., Ilya, S., and Geoffrey, H. E., ImageNet Classification with Deep Convolutional Neural Networks, Advances in neural information processing systems, pp. 1097-1105, 2012.
 Rumelhart, D., Hinton, G., and Williams, R., “Learning Representations by Back-Propagating Errors,” Nature, Vol. 323, pp. 533–536, 1986. [Online]. Available:
 Mathworks, “Transfer Learning Using GoogLeNet - MATLAB Simulink - MathWorks United Kingdom”, 2018. [Online]. Available: