Optical Character Recognition for Printed Tamizhi Documents using Deep Neural Networks

Monisha Munivel; V. S. Felix Enigo

doi:10.14429/djlit.42.4.17742

Monisha Munivel Department of Computer Science and Engineering, Sri SivaSubramaniya Nadar College of Engineering, Chennai, India https://orcid.org/0000-0002-5671-5094
V. S. Felix Enigo Department of Computer Science and Engineering, Sri SivaSubramaniya Nadar College of Engineering, Chennai, India https://orcid.org/0000-0002-1395-4722

DOI: https://doi.org/10.14429/djlit.42.4.17742

Keywords: Tamizhi script, Tesseract OCR, Tamizhi documents, CNN-RNN-CTC networks

Abstract

Tamizhi (Tamil-Brahmi) script is one of the oldest scripts in India from which most of the modern Indian scripts are evolved. The ancient historical documents are generally preserved as digitised texts using Optical Character Recognition (OCR) technique. But the development of OCR for Tamizhi documents is highly challenging as many characters have similar shapes and structures with very small variations. In specific, for Tamizhi script it is very difficult to build an OCR as many characters are combined characters. This can be a single character formed by a single vowel/consonant or compound characters formed by combining vowels and consonants. This paper deals with the development of Tamizhi OCR for printed Tamizhi documents which is anticipated to perform efficiently irrespective of poor quality, noises and various input formats of Tamizhi documents. This is a preliminary study towards developing an OCR for handwritten Tamizhi inscription images that recognises text captured from onsite inscriptions. The developed Tamizhi OCR for printed text can produce an accuracy of about 91.12 per cent.