Comparison of Visual and Logical Character Segmentation in Tesseract OCR Language Data for Indic Writing Scripts

Comparison of Visual and Logical Character Segmentation in Tesseract OCR Language Data for Indic Writing Scripts Jennifer Biggs National Security & Intelligence, Surveillance & Reconnaissance Division Defence Science and Technology Group Edinburgh, South Australia {firstname.lastname@dsto.defence.gov.au} Abstract Language data for the Tesseract OCR system currently supports recognition of a number of languages written in Indic writing scripts. An initial study is de- scribed to create comparable data for Tesseract training and evaluation based on two approaches to character segmen- tation of Indic scripts; logical vs. visual. Results indicate further investigation of visual based character segmentation lan- guage data for Tesseract may be warrant- ed. 1 Introduction The Tesseract Optical Character Recognition (OCR) engine originally developed by Hewlett- Packard between 1984 and 1994 was one of the top 3 engines in the 1995 UNLV Accuracy test as “HP Labs OCR” (Rice et al 1995). Between 1995 and 2005 there was little activity in Tesser- act, until it was open sourced by HP and UNLV. It was re-released to the open source community in August of 2006 by Google (Vincent, 2006), hosted under Google code and GitHub under the tesseract-ocr project. 1 More recent evaluations have found Tesseract to perform well in compar- isons with other commercial and open source OCR systems (Dhiman and Singh. 2013; Chatto- padhyay et al. 2011; Heliński et al. 2012; Patel et al. 2012; Vijayarani and Sakila. 2015). A wide range of external tools, wrappers and add-on pro- jects are also available including Tesseract user 1 The tesseract-ocr project repository was archived in Au- gust 2015. The main repository has moved from https://code.google.com/p/tesseract-ocr/ to https://github.com/tesseract-ocr interfaces, online services, training and training data preparation, and additional language data. Originally developed for recognition of Eng- lish text, Smith (2007), Smith et al (2009) and Smith (2014) provide overviews of the Tesseract system during the process of development and internationalization. Currently, Tesseract v3.02 release, v3.03 candidate release and v3.04 devel- opment versions are available, and the tesseract- ocr project supports recognition of over 60 lan- guages. Languages that use Indic scripts are found throughout South Asia, Southeast Asia, and parts of Central and East Asia. Indic scripts descend from the Brāhmī script of ancient India, and are broadly divided into North and South. With some exceptions, South Indic scripts are very rounded, while North Indic scripts are less rounded. North Indic scripts typically incorporate a horizontal bar grouping letters. This paper describes an initial study investi- gating alternate approaches to segmenting char- acters in preparing language data for Indic writ- ing scripts for Tesseract; logical and a visual segmentation. Algorithmic methods for character segmentation in image processing are outside of the scope of this paper. 2 Background As discussed in relation to several Indian lan- guages by Govandaraju and Stelur (2009), OCR of Indic scripts presents challenges which are different to those of Latin or Oriental scripts. Recently there has been significantly more pro- gress, particularly in Indian languages (Krishnan et al 2014; Govandaraju and Stelur. 2009; Yadav et al. 2013). …

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