A Novel Approach to OCR using Image Recognition based Classification for Ancient Tamil Inscriptions in Temples

TL;DR

This paper presents a new OCR method for ancient Tamil inscriptions using a CNN-based classification approach, achieving 77.7% accuracy, and integrates text-to-speech for audio output.

Contribution

It introduces a CNN-based OCR system specifically tailored for ancient Tamil script, utilizing a curated dataset and integrating Tesseract and Google's TTS for enhanced recognition and output.

Findings

Achieved 77.7% recognition accuracy on ancient Tamil inscriptions.

Developed a curated dataset of temple inscription characters.

Integrated OCR with text-to-speech for audio digitization.

Abstract

Recognition of ancient Tamil characters has always been a challenge for epigraphers. This is primarily because the language has evolved over the several centuries and the character set over this time has both expanded and diversified. This proposed work focuses on improving optical character recognition techniques for ancient Tamil script which was in use between the 7th and 12th centuries. While comprehensively curating a functional data set for ancient Tamil characters is an arduous task, in this work, a data set has been curated using cropped images of characters found on certain temple inscriptions, specific to this time as a case study. After using Otsu thresholding method for binarization of the image a two dimensional convolution neural network is defined and used to train, classify and, recognize the ancient Tamil characters. To implement the optical character recognition techniques, the neural network is linked to the Tesseract using the pytesseract library of Python. As an added feature, the work also incorporates Google's text to speech voice engine to produce an audio output of the digitized text. Various samples for both modern and ancient Tamil were collected and passed through the system. It is found that for Tamil inscriptions studied over the considered time period, a combined efficiency of 77.7 percent can be achieved.