Nepali OCR
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Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
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.
Description
The Nepali OCR project aims to develop a system that can detect and recognize 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.