A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

Htwe Pa Pa Win (University of Computer Studies-Yangon, Myanmar), Phyo Thu Thu Khine (University of Computer Studies-Yangon, Myanmar) and Khin Nwe Ni Tun (University of Computer Studies-Yangon, Myanmar)
Copyright: © 2012 |Pages: 26
DOI: 10.4018/ijcvip.2012010102
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This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.
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1. Introduction

Optical Character Recognition, OCR system plays an important role in Document Image Analysis System. The reason for this is that the primary goal of document analysis and recognition is to transform a paper document into a digital file with as less information loss as possible. To get the high performance of the OCR system, selection of the effective feature extraction methods is one of the most important facts that need to be done.

Features extraction is one of the most important factors in achieving high recognition performance in character recognition systems. The extracted features must be invariant to the expected distortions and variations that character may have in a specific application. Also, the number of features must be kept reasonably small if a statistical classifier is to be used (curse of dimensionality) (Duda, Hart, & Stork, 2001).

A feature extraction method that proves to be successful in one application domain may turn out not to be very useful in another domain. Furthermore, the type of features extracted must match the requirements of the chosen classifier.

There are both many built-in methods and various invention techniques for Feature Extraction of the different OCR systems. All of these methods can be categorized into two types of features: statistical, derived from statistical distribution of points and structural. The most common statistical features used for character representation are: zoning, projections and crossings and distances. Structural features are based on topological and geometrical properties of the character (Vamvakas, Gatos, & Perantonis, 2009). Furthermore, global transformations techniques such as Gabor and Hough Transformations are now becoming popular in some recognition systems. All different types of feature extraction methods are surveyed in Trier, Jain, and Taxt (1996) for the previous century and illustrated that the best method for each application domain can’t be the best for all applications.

The successful usage of statistical features are described in Rajashekararadhya and Ranjan (2005) and Kumar (2010) the former used 50 features and the later used considerable large amounts of features. For the printed Gurmukhi Script (Jindal, Sharma, & Sharma, 2008) and historical documents (Vamvakas, Gatos, & Perantonis, 2009) structural features are used depending on the nature of scripts and by subdividing the character image. They also show their best results. Features from Gabor filters are used mostly in scrip identification in bilingual documents and in Ramanathan, Ponmathavan, Thaneshwaran, Nair, Valliappan, and Soman (2009), Borji and Hamidi (2007), and Ramanathan Nair, Thaneshwaran, Ponmathavan, Valliappan, and Soman (2009), they also selected for their OCR systems.

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