Paper Title
SIGN LANGUAGE DETECTION USING IMAGE PROCESSING USING PYTHON
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Registration ID: IJNRD_305332
Published ID: IJNRD2504220
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Abstract
A crucial form of communication for those with speech and hearing problems is sign language. However, there remains a communication gap between the deaf community and non-signers because sign language is not widely understood by the general public. To close this gap, an automated sign language detection system utilizing deep learning and image processing methods in Python is presented in this study. The suggested system uses a webcam to record hand movements, which are then processed using OpenCV for picture preprocessing. To improve the quality of gesture images, methods such edge recognition, contour analysis, Gaussian blur, grayscale conversion, and background subtraction are used. A convolutional neural network (CNN) trained on a dataset of sign language motions is then used to classify the processed images. TensorFlow and Keras are used in the development of the CNN model, which enables effective feature extraction and hand sign categorization. The system can recognize basic words, numbers, and alphabets in American Sign Language (ASL), and it can be modified to recognize other sign language databases. The system continually records frames from the video stream, extracts hand area information, and predicts the related sign language symbols for real-time sign detection. Using Tkinter or Streamlit, a graphical user interface (GUI) is also included to show the identified signs in text format. Accessibility for those with hearing impairments can also be improved by integrating Text-to-voice (TTS) technology, which translates recognized signals into voice.
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How To Cite (APA)
TAMIL SELVAN P, VEERAVEL E, RAM KARTHIK S, SANJEEV REVANTH R, & SHARON NISHA M (April-2025). SIGN LANGUAGE DETECTION USING IMAGE PROCESSING USING PYTHON. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), b846-b851. https://ijnrd.org/papers/IJNRD2504220.pdf
Issue
Volume 10 Issue 4, April-2025
Pages : b846-b851
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Paper Reg. ID: IJNRD_305332
Published Paper Id: IJNRD2504220
Research Area: Science and Technology
Author Type: Indian Author
Country: Tirunelveli, Tamilnadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2504220.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2504220
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