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Non-linear Normalization to Improve Telugu OCR

Atul Negi, Chakravarthy Bhagvati, V.V. Suresh Kumar

Department of Computer and Information Sciences,

University of Hyderabad

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Acknowledgements

Ministry of Information Technology, New Delhi Under the Project

Resource Center for Indian Language Technology Solutions (Telugu)

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Organization of Presentation• Introduction• Telugu Script• Classification By Template Matching• Complete OCR Algorithm• Nonlinear Normalization• Results• Concluding Remarks• Bibliography• Contact Information

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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

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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)

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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

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Few Telugu Characters

• Achchus

• Hallus

• Maatras

• Voththus

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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

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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

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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

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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).

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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

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Example

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Normalized Glyphs

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Results

0

100

200

300

400

500

Image 1 Image 2 Image 3 Image 4 Image 5

Fig. 5. Graphical representation of comparision of Linear and Non-linear Normalization

Number Glyphs Linear Normalization Non-Linear Normalization

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Image 1Misclassifications: 1 (NSN) , 7 (L)

Total Glyphs: 145 ( 99%, 95.2% )

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Image 5Misclassifications:

• 105 (NSN)

• 136 (linear Normalization)

Total Glyphs: 354 (70.3%, 61.6%)

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Discussion

•Why Should Nonlinear Normalization succeed despite shape distortions?

•Is the best that we can do?

•Why not use this always?

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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

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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).

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Contact Information

Atul Negi, Chakravarthy BhagvatiDepartment of Computer and Information Sciences,

University of Hyderabad

Hyderabad 500 046, AP INDIA

Email: atulcs@uohyd.ernet.in

Visit http://www.uohyd.ernet.in

and http://www.Languagetechnologies.ac.in