International Scientific Journal of Engineering and Management

An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata
ISSN: 2583-6129

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Anusha C, Saket Mishra, Rohit Metre, Harsh Gurawalia

Department of CSE, PES University, Bangalore-79, Karnataka

Email: anusha20c@gmail.com, saketmishra113@gmail.com, rohitmetre2000@gmail.com, harshrocks2442@gmail.com

Contact: +91 7676240083, +91 9731963460, +91 9611910110, +91 9901360787

Guided By:

Dr.SapnaV.M, Assistant Professor, Dept. of CSE, PES UNIVERSITY,Bangalore,Karnataka

Email: sapnavm@pes.edu

Abstract:. We'll be putting together an OCR system pipeline. A Convolutional neural network will be used to classify each individual character. CNN requires less training than a fully linked network because it has fewer parameters. To make this work, we'll first split the lines, then the words, and ultimately the individual characters that will be sent to CNN. The English character dataset that has been acquired will be used to train the CNN. The EMNIST dataset (Extended Modified National Institute of Standards and Technology) has around 8 lakh samples divided into 62 classes (10 digits + 26 lowercase alphabets + 26 uppercase alphabets). We discovered another CHARS74k dataset since this dataset comprises handwritten characters. CHARS74k has 62 classes, identical to EMNIST, and is a normalized dataset with 1016 samples for each character class. To build words, we will merge the expected character label from CNN. It's possible that the prediction is inaccurate and contains some misclassification. As a result, some adjustments are required. To accomplish this, we will utilize an English word spell checker to locate all similar words and select the most appropriate one.

 

 


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