Evaluating Performance of Nepali Script OCR using Tesseract and Artificial Neural Network

This paper evaluates the performance of Nepali Script OCR using Tesseract and ANN. A dataset of 69 Nepali fonts with the 2,484 character samples of consonants was used in the study. With Tesseract, the overall accuracy of 96% was obtained in the training phase and 69% in the testing phase. Similarly...

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Bibliographic Details
Published in:	2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS) pp. 104 - 107
Main Authors:	Prajapati, Sudan, Joshi, Shashidhar Ram, Maharjan, Aman, Balami, Bikash
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-10-2018
Subjects:	ANN Artificial neural networks Backpropagation Character recognition Character Segmentation Feature extraction Feedforward Neural Network Image segmentation Nepali Font Nepali Script Optical Character Recognition Optical character recognition software Tesseract Testing Training
Online Access:	Get full text
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Summary:	This paper evaluates the performance of Nepali Script OCR using Tesseract and ANN. A dataset of 69 Nepali fonts with the 2,484 character samples of consonants was used in the study. With Tesseract, the overall accuracy of 96% was obtained in the training phase and 69% in the testing phase. Similarly, with ANN, an accuracy of 98% was obtained in training and 81% in testing.
DOI:	10.1109/CCCS.2018.8586808