Informatics Publishing Limited

K. Indira ¹, Anjali Kulkarni ², S. Sethu Selvi ¹

Affiliations
1 Department of Electronics and Communication Engineering, M.S. Ramaiah Institute of Technology, Bangalore, IN
2 Department of Electronics and Communication Engineering, Atria Institute of Technology, Bangalore, IN

Abstract

Text line segmentation is an essential pre-processing stage for off-line handwriting recognition in many Optical Character Recognition (OCR) systems. It is an important step, as inaccurately segmented text lines will cause errors in the subsequent recognition stage. Text line segmentation of handwritten documents is still one of the most complicated problems in developing a reliable OCR. The nature of handwriting styles makes the process of text line segmentation very challenging. This paper provides a means of text line segmentation using Vertical Strip Projection based method. In this paper, the document is divided into vertical strips of appropriate width. Strip-wise horizontal histograms are then computed and the relationship of the peak valley points is used for line segmentation. Though the global horizontal projection method works efficiently for printed documents, it becomes highly unreliable when used in case of unconstrained handwritten text because of many factors like variable skew, touching and overlapping lines. The proposed method provides much improved and reliable results when compared to global horizontal profile method in case of skewed and variable sloping lines. Segmentation accuracy of 83% is achieved for handwritten Kannada text using the proposed method.

Keywords

Horizontal Projection Profile (HPP), Line Segmentation, Piecewise Separating Lines (PSL), Vertical Strip Projection.

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Maya V. Karki ¹, S. Sethu Selvi ²

Affiliations
1 Department of Electronics and Communication Engineering, MSRIT, IN
2 Department of Electronics and Communication, MSRIT, IN

Abstract

Establishing the identity of a person with highconfidence using biometric systems are gaining importance. It is achallenge to improve the recognition rate of an existing unimodalbiometric system. Fingerprint recognition is one of the most matureand proven technology because of its immutability and individuality.Recognition result of the system is based mainly on feature extractionmethod and type of matcher used. This paper proposes amulti-algorithm fusion algorithm for fingerprint recognition. The mainobjective of the proposed system is to improve performance usingtexture features. Feature extraction is based on ridge information andtextures. Orientation features, Curvelet transform features and DualTree-Complex Wavelet Transform (DT-CWT) features are extractedand using Euclidean distance match scores are evaluated. Texturefeatures need very less pre-processing compared to orientationfeatures. With this speed of the recognition system is improved.Weighted sum method is used in fusion of matchers. Performance ofindividual matchers in terms of False Acceptance Rate (FAR) andFalse Reject Rate (FRR) has been evaluated. For optimal threshold(η),percentage genuine recognition rate (%GAR) is calculated. Algorithm is tested on fingerprint database of 100 users and also with FVC2002-DB3 database. Maximum recognition rate of 95.2% is achieved by combining Curvelet and DT-CWT features.

Keywords

Curvelet Transform, DT-CWT, Orientation Features Multi-Algorithm Fusion.

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