Abstract
A major requirement in the design of robust OCRs is the invariance of feature extraction scheme with the popular fonts used in the print. Many statistical and structural features have been tried for character classification in the past. In this paper, we get motivated by the recent successes in object category recognition literature and use a spatial extension of the histogram of oriented gradients (HOG) for character classification. Our experiments are conducted on 1453950 Telugu character samples in 359 classes and 15 fonts. On this data set, we obtain an accuracy of 96-98% with an SVM classifier.
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References
Sankar, K.P., Ambati, V., Pratha, L., Jawahar, C.V.: Digitizing a million books: Challenges for document analysis. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 425–436. Springer, Heidelberg (2006)
Neeba, N.V., Jawahar, C.V.: Empirical evaluation of character classification schemes. In: Seventh International Conference on Advances in Pattern Recognition (ICAPR), pp. 310–313. IEEE, Los Alamitos (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893. IEEE, Los Alamitos (2005)
Negi, A., Bhagvati, C., Krishna, B.: An OCR system for telugu. In: Proceedings of Sixth International Conference on Document Analysis and Recognition (ICDAR), pp. 1110–1114. IEEE, Los Alamitos (2002)
Jawahar, C.V., Kumar, P., Kiran, R., et al.: A blingual OCR for hindi-telugu documents and its applications. In: Proceedings of Seventh International Conference on Document Analysis and Recognition (ICDAR), pp. 408–412. IEEE, Los Alamitos (2003)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2169–2178. IEEE, Los Alamitos (2006)
Maji, S., Malik, J.: Fast and accurate digit classification. Technical Report UCB/EECS-2009-159, EECS Department, University of California, Berkeley (2009)
De Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. In: Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP), INSTICC, pp. 273–280 (2009)
Ilayaraja, P., Neeba, N.V., Jawahar, C.V.: Efficient implementation of SVM for large class problems. In: 19th International Conference on Pattern Recognition (ICPR), pp. 1–4. IEEE, Los Alamitos (2009)
Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE, Los Alamitos (2008)
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Rasagna, V., Jinesh, K.J., Jawahar, C.V. (2011). On Multifont Character Classification in Telugu. In: Singh, C., Singh Lehal, G., Sengupta, J., Sharma, D.V., Goyal, V. (eds) Information Systems for Indian Languages. ICISIL 2011. Communications in Computer and Information Science, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19403-0_14
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DOI: https://doi.org/10.1007/978-3-642-19403-0_14
Publisher Name: Springer, Berlin, Heidelberg
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