Abstract
Many techniques have been reported for handwritten based document image retrieval. This paper proposes a novel method by using Contourlet Transform (CT) for feature extraction of document images which achieves high retrieval rate. The handwriting of different people is often visually distinctive; we take a global approach based on texture analysis, where each writer’s handwriting is regarded as a different texture. The Canberra distance is used as similarity measure in proposed system. A retrieval result with proposed method is very promising with precision and recall as compared to the existing system.
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Shirdhonkar, M.S., Kokare, M.: Handwritten Document Image Retrieval. In: Proceeding of 3rd International Conference on Computer Modeling and Simulation (ICCMS 2011), Mumbai, India, pp. VI-506–VI-510 (2011)
Zhang, B., Srihari, S.N.: Binary Vector Dissimilarly Measures for Handwriting Identification. In: Document Recognition and Retrieval. SPIE, pp. 28–38 (2003)
Schomaker, L., Bulacu, M.: Automatic Writer Identification Using Connected Component Contours and Edge Based Features of Uppercase Western Script. IEEE Trans. of Pattern Analysis and Machine Intelligence 26(6), 787–798 (2004)
Bensefia, A., Paquet, T., Heutte, L.: A Writer Identification and Verification System. Pattern Recognition Letters 26(13), 2080–2092 (2005)
Pareti, R., Vincent, N.: Global Method Based on Pattern Occurrences For Writer Identification. In: Proc. of the 10th International Workshop on Frontiers in Handwriting Recognition, La Baule, France (2006)
Joutel, G., Eglin, V., Bres, S., Emptoz, H.: Curvelets Based Features Extraction of Handwritten Shapes for Ancient Manuscript Classification. In: Proc. Document Recognition and Retrieval XIV, SPIE –IS&T Electronic imaging. SPIE, vol. 6500, pp. 65000D-1–65000D-10 (2007)
Siddiqi, I., Vincent, N.: Combining Global and Local Features for Writer Identification in Handwritten Documents. In: Proc. of the 11th International Conference on Frontiers in Handwriting Recognition, Canada (2008)
Siddiqi, I., Vincent, N.: A Set of Chain Code Based Features for Writer Recognition. In: 10th International Conference on Document Analysis and Recognition, pp. 981–985 (2009)
Ning, L., Zhou, L., You, X., Du, L., He, Z.: Multiscale Gaussian Markov Random fields for writer identification. In: Proc. of International Conference on Wavelet Analysis and Pattern Recognition, pp. 170–175 (2010)
Srinivasa Rao, C., Srinivas Kumar, S., Chatterji, B.N.: Content Based Image Retrieval Using Contourlet Transform. ICGST-GVIP Journal 7(3) (2007)
Po, D.D.Y., Do, M.N.: Directional Multiscale Modeling of Image Using the Countourlet Transform. IEEE Transactions on Image Processing 15(6), 1610–1620 (2006)
Kokare, M., Biswas, P.K., Chatterji, B.N.: Texture Image Retrieval Using New Rotated Complex Wavelets Filters. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics 35(6), 1168–1178 (2005)
Bamberger, R.H., Smith, M.J.T.: A Filter Bank for the Directional Decomposition of Images: Theory and design. IEEE Transactions on Signal Processing 40(4), 882–893 (1992)
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Shirdhonkar, M.S., Kokare, M.B. (2011). Writer Based Handwritten Document Image Retrieval Using Contourlet Transform. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Digital Image Processing and Information Technology. DPPR 2011. Communications in Computer and Information Science, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24055-3_11
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DOI: https://doi.org/10.1007/978-3-642-24055-3_11
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