Abstract
A population of 430 million people and above, or over a population of 5% of the world’s population, needs therapy to treat their “disabled” hearing and speaking condition. These people have the option to learn sign language to communicate with others. Hence, our project mainly targets the deaf and mute community. Around 5000 images of hand gestures have been used and divided into 10 categories for live detection. The categories are mainly American Sign Language (ASL) and are consisted of the first 10 numbers. Our model can detect these ten hand motions and categorize them correctly. We used the You Only Look Once Version 5 algorithm. The algorithm consists of a backbone namely CSPDarknet53, in which an SPP block is accustomed to accelerating the speed of the receptive field responsible to set apart prime traits and confirming that network operation speed is inclining in speed. The neck of the algorithm, PAN, is added to aggregate the parameters from different backbone levels. This model is very easy to use and understand and gives an accuracy above 98%. That is why we chose YoloV5 as our model for object detection due to its simplicity in usage. Therefore, an artificial sign language detection system has been suggested in this study which incorporates deep learning and image processing method. This study also gives a comparison between the two models to give a better understating of why we marked YoloV5 as a better algorithm even though both models gave an accuracy of above 98%. We believe that making a hand gesture detection system will encourage individuals to communicate with people who cannot hear or speak. That being the case, we aim to make the lives of the disabled better.
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References
Ali, S.M.: Comparative analysis of YoloV3, YoloV4, and YoloV5 for sign language detection. Department of Information Technology, Rajagiri School of Engineering and Technology, Kerala, India (2021)
Barbhuiya, A.A., Kash, R.K., Jain, R.: CNN-based feature extraction and classification for sign language. Multimed. Tools Appl. 80, 3051–3069 (2021)
Singh, S., Jain, S.: Factors associated with deaf-mutism in children attending special schools of rural central India: a survey. J. Fam. Med. Primary Care 9(7), 3256 (2020)
Hasan, M.M., Misra, P.K.: HSV brightness factor matching for gesture recognition system. IJIP 4(5), 456–467 (2011)
Nagarajan, S., Subashini, T.S.: Static hand gesture recognition for sign language alphabets using edge-oriented histogram and multi-class SVM. Int. J. Comput. Appl. 82(4), 28–35 (2013)
Vedak, O., Zavre, P., Todkar, A., Patil, M.: Sign language interpreter using image processing and machine learning. Department of Computer Engineering, Datta Meghe College of Engineering, Mumbai University, Airoli, India (2019)
Plouffe, G., Cretu, A.M.: Static and dynamic hand gesture recognition in-depth data using dynamic time warping. IEEE Trans. Instrum. Meas. 65(2), 305–316 (2015)
Jadooki, S., Mohamad, D., Saba, T., Almazyad, A.S., Rehman, A.: Fused features mining for depth-based hand gesture recognition to classify blind human communication. Neural Comput. Appl. 28(11), 3285–3294 (2016). https://doi.org/10.1007/s00521-016-2244-5
Li, Y., Wang, X., Liu, W., Feng, B.: Deep attention network for joint hand gesture localization and recognition using static RGB-D images. Inf. Sci. 441, 66–78 (2018)
Sign language recognition with multi-feature fusion and ANN class
Badi, H.: Recent methods in vision-based hand gesture recognition. Int. J. Data Sci. Anal. 1(2), 77–87 (2016). https://doi.org/10.1007/s41060-016-0008-z
Archana, S., Gajanan, K.: Hand segmentation techniques to hand gesture recognition for natural human-computer interaction. ACM Trans. Interact. Intell. Syst. 3, 15 (2012)
Braffort, A.: Research on computer science and sign language: ethical aspects. In: Wachsmuth, I., Sowa, T. (eds.) Gesture and Sign Language in Human-Computer Interaction, GW 2001, vol. 2298, pp. 1–8. Springer, Berlin (2002). https://doi.org/10.1007/3-540-47873-6_1
Dabre, K., Dholay, S.: Machine learning model for sign language interpretation using webcam images. Department of Computer Engineering Sardar Patel Institute of Technology Student of M.E. (Computer) Mumbai, India (2014)
Suharjitoa, R.A., Wiryanab, F., Ariestab, M.C., Kusumaa, G.P.: Sign language recognition application systems for deaf-mute people: a review based on input-process-output. Comput. Sci. 116, 441–448 (2017)
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Azim, N., Jamema, S.A., Sultana, N. (2023). Study and Analysis of Deep Learning Models for the Recognition of Sign Language. In: Garg, D., Narayana, V.A., Suganthan, P.N., Anguera, J., Koppula, V.K., Gupta, S.K. (eds) Advanced Computing. IACC 2022. Communications in Computer and Information Science, vol 1782. Springer, Cham. https://doi.org/10.1007/978-3-031-35644-5_29
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