Nepali OCR

Abstract

handwritten Nepali texts. Our system’s architecture consists of a Convolutional Neural Network (CNN) for feature extraction and a Recurrent Neural Network (RNN) for sequence recognition. The training dataset consists of around 80,000 Nepali handwritten images, which are preprocessed and augmented to increase the system’s robustness. NepaliOCR can be used to convert printed or handwritten Nepali text into editable digital formats, making it useful for a range of applications such as document digitization, language learning, and natural language processing. This paper presents an overview of the NepaliOCR system, including its architecture, training methodology, and performance evaluation. The lack of a reliable tool for Nepali handwriting recognition motivated us to develop this system. The system is developed as a part of the Bachelor in Computer Engineering Major Project. Machine learning has been used in the system to overcome the limitations of traditional computer systems. Our attempt to develop a tool for Nepali Handwriting Recognition using Machine Learning is discussed in this report. With the recent advancement in machine learning, our system shows great potential for practical applications in the digitization of Nepali handwriting.