25th June 2002IEMCT CDAC Pune1 Non-linear Normalization to Improve Telugu OCR Atul Negi, Chakravarthy Bhagvati, V.V. Suresh Kumar Department of Computer and Information Sciences, University of Hyderabad

25th June 2002IEMCT CDAC Pune2 Acknowledgements Ministry of Information Technology, New Delhi Under the Project Resource Center for Indian Language Technology Solutions (Telugu)

25th June 2002IEMCT CDAC Pune3 Organization of Presentation Introduction Telugu Script Classification By Template Matching Complete OCR Algorithm Nonlinear Normalization Results Concluding Remarks Bibliography Contact Information

25th June 2002IEMCT CDAC Pune4 Introduction OCR Research Indian Scripts – Initial era Pioneers: RMK Sinha, Deekshitalu, ISI Kolkata –Maturity: Mid Nineties Complete Systems Bangla Devanagari Recent Status of OCR in Indian Scripts –ICDAR 1999, Bangalore –ILOCR Workshop, 2002 UoH –Sadhana, Indian Acad. Sci. Feb `02, Special Issue

25th June 2002IEMCT CDAC Pune5 Introduction: Progress of Telugu OCR Structural approach (ref. 4), –moments and size of the character used Neural networks (ref. 1), –Connected Components, training and recognition Template Matching (ref. 5), –Connected Components, Templates and linear size normalization Wavelet multi-resolution analysis (ref. 6)

25th June 2002IEMCT CDAC Pune6 Telugu Script Features of Telugu –Basic vowel sounds (Acchulu) 16 symbols –Simple consonants (Hallulu) 36 symbols –Vowel Sounds (Matraas) 16 symbols –Half Consonants (Voththus) 30 symbols Complexity of Character Recognition –Composition of Characters and Syllables from above symbols: 5000 or so in common use. Reducing Complexity –Identification of glyphs used in composition : about 400

25th June 2002IEMCT CDAC Pune7 Few Telugu Characters Achchus Hallus Maatras Voththus

25th June 2002IEMCT CDAC Pune8 Classification By Template Matching Why Template Matching? –Feature Extraction Effectiveness –Dimensionality (Size 32x32) Fringe Distances (ref. 10) –No need for blurring –Distances Pre-computed and Stored –Ease of matching

25th June 2002IEMCT CDAC Pune9 The Complete OCR algorithm Read an input binary image Segment the image into words Extract the connected components from each word For each component –(a) Normalize size to match stored templates –(b) Compute fringe distance map –(c) Compute fringe distance from all templates –(d) Output template with smallest fringe distance –(e) Convert template code to ISCII Store ISCII output in a file

25th June 2002IEMCT CDAC Pune10 Nonlinear Normalization Need for Normalization –Preprocessing step to equalize size, position, inclination etc. to ease recognition –Necessary when recognition is by template matching Non-Linear Normalization –All parts of the character image not treated equally –Hypothesis: Differences between characters will be increased, therefore improved discrimination

25th June 2002IEMCT CDAC Pune11 Nonlinear Normalization Technique Line density equalization-analogous to histogram density equalization (ref. 13) Generalization : Feature Density Equalization (ref. 14) –Projection of feature density onto horizontal, vertical axes –Feature projection functions H(i) and V(j) –input, i=1,…I and j=1,…J. –new position (m, n) output computed in normalized image of size (M,N) for point (i, j) in input image of size (I,J).

25th June 2002IEMCT CDAC Pune12 Nonlinear Normalization Technique Feature Density Equalization –Feature projection functions H(k) and V(l), input, i=1,…I and j=1,…J. –New position (m, n) output size (M,N), for each point (i, j) in input image of size (I,J). –m=  k=1 to i H(k)  M / [  k=1 to i H(k)] –n=  l=1 to j V(l)  N / [  l=1 to j V(l)] –H(i)=  (j=1 to J) f(i, j) +  H –V(j)=  (i=1 to I) f(i, j) +  V { NSN by dot density

25th June 2002IEMCT CDAC Pune13 Example

25th June 2002IEMCT CDAC Pune14 Normalized Glyphs

25th June 2002IEMCT CDAC Pune15 Results

25th June 2002IEMCT CDAC Pune16 Image 1 Misclassifications: 1 (NSN), 7 (L) Total Glyphs: 145 ( 99%, 95.2% )

25th June 2002IEMCT CDAC Pune17 Image 5 Misclassifications: 105 (NSN) 136 (linear Normalization) Total Glyphs: 354 (70.3%, 61.6%)

25th June 2002IEMCT CDAC Pune18 Discussion Why Should Nonlinear Normalization succeed despite shape distortions? Is the best that we can do? Why not use this always?

25th June 2002IEMCT CDAC Pune19 Concluding Remarks Non-linear normalization appears to improve OCR accuracy (based on 1300 glyphs examined) More experimentation with the features is required to overcome problems like gaps Further testing on variety of fonts and sizes is required to conclude recognition improvement with more confidence

25th June 2002IEMCT CDAC Pune20 Bibliography M.B. Sukhswami, P. Seetharamulu, and Arun K. Pujari, “Recognition of Telugu characters using Neural networks,” Int. J. of Neural Systems, 6(3):317 (1995). R. Kasturi and S. N. Srihari (Eds.). Proc. Fifth International Conf. Document Anaalysis and Recognition. Bangalore, India, IEEE Computer Society Press, Los Alamitos, CA, (1999). B.B. Chaudhuri and U. Garain, and M. Mitra, “On OCR of the most popular two Indian language scripts: Devanagari and Bangla”, in Visual Text Recogntion and Document Processing, Ed. N. Murshed, World Scientific Press (2000). SNS Rajasekharan and B.L. Deekshatulu, “Generation and Recognition of Printed Telugu characters”, Computer Graphics and Image Processing, 6: , (1977). Atul Negi, Chakravarthy Bhagvati, and B. Krishna, “An OCR system for Telugu”, Proc.. Sixth International Conf. Document Analysis and Recognition. Seattle, USA, IEEE Computer Society Press, Los Alamitos, CA, (2001). A.K. Pujari, C.D. Naidu, and B.C.Jinaga, “An addaptive and intelligent character recognizer for Telugu scripts using multiresolution analysis and associative memory”, Proc. Canadian Conf. On AI, Calagary, Canada, May 2002, LNCS, Springer Verlag (2002). B. Krishna, “Design and implementation of a Telugu script recognition system” Technical report, Dept. of Computer and Information Sciences, University of Hyderabad, Hyderabad, India, (2000). R.C. Gonzalez and R.E. Woods. Digital Image Processing. Addison-Wesley, 1993 O.D. Trier, A.K. Jain, and R.Taxt. “Feature extraction methods for character recognition-a survey”, Pattern Recognition, 29(4): , (1996). R.L. Brown. “The fringe distance measure: an easily calculated image distance measure with recognition results comparable to Gaussian blurring”, IEEE Trans. System Man and Cybernetics, 24(1): , (1994). K. Wong, R. Casey, and F. Wahl. “Document analysis system”. IBM J. Research and Development, 26(6), (1982). G. Nagy, S. Seth, and M. Vishwanathan, “A prototype document image analysis system for technical journals” Computer, 25(7), (1992). H. Yamada, K. Yamamoto and T. Saito, “A nonlinear normalization method for handprinted Kanji character recognition-line density equalization”, Pattern Recognition, 23(9): , (1990). S-W. Lee and J-S. Park, “Nonlinear shape normalization methods for the recognition of large set handwritten characters”, Pattern Recognition, 27(7): , (1994). V.V. Suresh Kumar, “Non-linear Normalization Techniques to Improve OCR”, Technical report, Dept. of Computer and Information Sciences, University of Hyderabad, Hyderabad, India,(2002).

25th June 2002IEMCT CDAC Pune21 Contact Information Atul Negi, Chakravarthy Bhagvati Department of Computer and Information Sciences, University of Hyderabad Hyderabad , AP INDIA Visit and