Ocr for Hindi Language

Registration ID: IJNRD_192170

Published ID: IJNRD2304454

DOI: Click Here to Get

Prof.S.Sankareswari , Tushar Narayan Margale , Ganesh Chandrakant Arondekar , Nehal Vasant Dhuri , Shruti Santosh Desai

OCR, Pre-processing, Segmentation, Feature Vector, Classification, Artificial Neural Network (ANN)

With over 300 million speakers, Hindi is India's most spoken language. The Optical Character Recognition (OCR) systems designed for the Hindi language have a very low recognition rate due to the lack of character separation in Hindi texts compared to English texts. An Artificial Neural Network (ANN)-based OCR for printed Hindi text written in Devanagari script is proposed in this paper to increase its efficiency. One of the significant purposes behind the unfortunate acknowledgment rate is blunder in character division. The fact that the scanned documents contain touching characters makes the process of segmentation even more difficult. As a result, designing an efficient method for character segmentation presents a significant challenge. A general OCR consists of preprocessing, character segmentation, feature extraction, classification, and recognition at the end. The paper looks at the preprocessing tasks of converting grayscaled images to binary images, rectifying images, and segmenting the text of the document into paragraphs, lines, words, and then basic symbols. The neural classifier recognizes the fundamental symbols that were obtained as the fundamental unit through the segmentation process. In this work, three element extraction procedures : histogram of projection in light of mean distance, histogram of projection in light of pixel worth, and vertical zero intersection, have been utilized to work on the pace of acknowledgment. Even distorted characters and symbols can have their features extracted using these powerful feature extraction methods. A back-propagation neural network with two hidden layers is used to build the neural classifier. For printed Hindi texts, the classifier is trained and tested. It is possible to achieve a performance with a correct recognition rate of roughly 90%.

Published Paper   E-Certificate

Prof.S.Sankareswari, Tushar Narayan Margale, Ganesh Chandrakant Arondekar, Nehal Vasant Dhuri, & Shruti Santosh Desai (April-2023). Ocr for Hindi Language . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), e406-e410. https://ijnrd.org/papers/IJNRD2304454.pdf

Volume 8 Issue 4, April-2023

Pages : e406-e410

Paper Reg. ID: IJNRD_192170

Published Paper Id: IJNRD2304454

Downloads: 000122004

Research Area: Engineering

Country: Sawantwadi, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304454.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304454

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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