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A Convolution Neural Network for Optical Character Recognition and Subsequent Machine Translation

by Goutam Sarker, Swagata Ghosh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 30
Year of Publication: 2018
Authors: Goutam Sarker, Swagata Ghosh
10.5120/ijca2018918203

Goutam Sarker, Swagata Ghosh . A Convolution Neural Network for Optical Character Recognition and Subsequent Machine Translation. International Journal of Computer Applications. 182, 30 ( Dec 2018), 23-27. DOI=10.5120/ijca2018918203

@article{ 10.5120/ijca2018918203,
author = { Goutam Sarker, Swagata Ghosh },
title = { A Convolution Neural Network for Optical Character Recognition and Subsequent Machine Translation },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 182 },
number = { 30 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number30/30219-2018918203/ },
doi = { 10.5120/ijca2018918203 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:12:55.060537+05:30
%A Goutam Sarker
%A Swagata Ghosh
%T A Convolution Neural Network for Optical Character Recognition and Subsequent Machine Translation
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 30
%P 23-27
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Optical character recognition has been a longstanding challenging research topic in the broad area of machine learning and pattern recognition. In the present paper, we investigate the problem of textual image recognition and translation, which is among the most daunting challenges in image-based sequence recognition. A convolutional neural network (CNN) architecture, which integrates optical character recognition and natural language translation into a unified framework, is proposed. The accuracy of both OCR output and subsequent translation is moderate and satisfactory. The proposed system for OCR and subsequent translation is an effective, efficient and most promising one.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Recurrent CNN LSTM CNN based LSTM Performance Evaluation Accuracy

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