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Approaches to Recognition for Telugu Script: Fringe Distances and Other Methods
Atul Negi and Ravi Raj Singh Department of Computer and Information Sciences University of Hyderabad
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Optical Character Recognition
OCR is an enabling technology for languages Potential for office automation and content creation activities Transform paper media into searchable and computer revisable text format
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OCR in Indian Scripts -I
Complete Bangla OCR (Pal and Chaudhuri 1999) Devnagari and Bangla OCR (Chaudhuri et al 2000) Devanagari OCR (Bansal and Sinha 1999)
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OCR in Indian Scripts -II
Gurmukhi OCR (Lehal et al 2000) Tamil OCR ( AG Ramakrishna 2002, V Krishnamoorthy 2001) Kannada OCR (PS Sastry 1999, AG Ramakrishna 2002)
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Recognition Approaches
OCR Efforts in Telugu Recognition Approaches Rajasekharan and Deekshatulu 1977 Sukhswami, Seetharamulu , and Pujari 1995 Negi, Chakravarthy and Krishna 2001 Pujari et al 2002 Vasantha and Patvardhan 2002
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Telugu Script Difficulties
Rounded Nature Lack of vertical strokes Placement of ottulu and matra Merging of glyphs Fonts with varied stroke thickness
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Fringe Distances Template Matching Approach
Needs storage of Fixed Size Templates Connected components (CC) isolated from lines and words in the image Each input CC is scaled to Template size
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Fringe Formation Fringes are point distances to image pixels stored for each training template character. Fringes speed up template matching.
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Run Number Based Metric Distance
Technique used for Bangla/Devanagari JH(X, Y) = Σ |nX(i) – nY(i)| (1) J(X, Y) = JH(X, Y) + JV(X, Y) (2) Invariant to scaling Robust to font style changes
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Recognition Results Data Five image files More than 5000 input glyphs
Accuracy Comparison Data Five image files More than 5000 input glyphs
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Recognition Time Run on Linux system Pentium 3 system
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Conclusions Accuracy for fringe distances is marginally superior
Time taken by RNM is marginally superior Further testing required on very varied fonts styles to make definite statements Thank You
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