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A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents
August 2005 (vol. 27 no. 8)
pp. 1212-1225
Many libraries, museums, and other organizations contain large collections of handwritten historical documents, for example, the papers of early presidents like George Washington at the Library of Congress. The first step in providing recognition/retrieval tools is to automatically segment handwritten pages into words. State of the art segmentation techniques like the gap metrics algorithm have been mostly developed and tested on highly constrained documents like bank checks and postal addresses. There has been little work on full handwritten pages and this work has usually involved testing on clean artificial documents created for the purpose of research. Historical manuscript images, on the other hand, contain a great deal of noise and are much more challenging. Here, a novel scale space algorithm for automatically segmenting handwritten (historical) documents into words is described. First, the page is cleaned to remove margins. This is followed by a gray-level projection profile algorithm for finding lines in images. Each line image is then filtered with an anisotropic Laplacian at several scales. This procedure produces blobs which correspond to portions of characters at small scales and to words at larger scales. Crucial to the algorithm is scale selection, that is, finding the optimum scale at which blobs correspond to words. This is done by finding the maximum over scale of the extent or area of the blobs. This scale maximum is estimated using three different approaches. The blobs recovered at the optimum scale are then bounded with a rectangular box to recover the words. A postprocessing filtering step is performed to eliminate boxes of unusual size which are unlikely to correspond to words. The approach is tested on a number of different data sets and it is shown that, on 100 sampled documents from the George Washington corpus of handwritten document images, a total error rate of 17 percent is observed. The technique outperforms a state-of-the-art gap metrics word-segmentation algorithm on this collection.
[1] H.S. Baird and K. Thompson, “Reading Chess,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp. 552-559, 1990.
[2] R.M. Bozinovic and S.N. Srihari, “Off-Line Cursive Script Word Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 1, pp. 68-83, Jan. 1989.
[3] R.G. Casey and E. Lecolinet, “A Survey of Methods and Strategies in Character Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 690-706, July 1996.
[4] R.O. Duda and P.E. Hart, “Experiments in Recognition of Hand-Printed Text,” Am. Federation of Information Processing Soc. Conf. Proc., pp. 1139-1149, 1968.
[5] M. Feldbach and K.D. Tonnies, “Segmentation of the Date in Entries of Historical Church Registers,” Proc. Deutsche Arbeitsgemeinschaft für Mustererkennung Symp., pp. 403-410, 2002.
[6] L.M.J. Florack, The Syntactic Structure of Scalar Images. Kluwer Academic, 1997.
[7] V. Govindaraju and H. Xue, “Fast Handwriting Recognition for Indexing Historical Documents,” Proc. Workshop Document Image Analysis for Libraries, pp. 314-320, 2004.
[8] J. Ha, R.M. Haralick, and I.T. Phillips, “Document Page Decomposition by the Bounding-Box Projection Technique,” Proc. Int'l Conf. Document Analysis and Recognition, pp. 1119-1122, 1995.
[9] P.V.C. Hough, Method and Means for Recognizing Complex Patterns, US patent 3069654, Washington, D.C.: Patent and Trademark Office, 1962.
[10] G. Kim, V. Govindaraju, and S.N. Srihari, “An Architecture for Handwritten Text Recognition Systems,” Int'l J. Document Analysis and Recognition, pp. 37-44, 1999.
[11] J.J. Koenderink and A.J. van Doorn, “Surface Shapes and Curvatures Scales,” Image and Vision Computing, vol. 10, no. 8, 1992.
[12] V. Lavrenko, T.M. Rath, and R. Manmatha, “Holistic Word Recognition for Handwritten Historical Documents,” Proc. Workshop Document Image Analysis for Libraries, pp. 278-287, 2004.
[13] T. Lindeberg, Scale-Space Theory in Computer Vision. Kluwer Academic, 1994.
[14] U. Mahadevan and R.C. Nagabushnam, “Gap Metrics for Word Separation in Handwritten Lines,” Proc. Int'l Conf. Document Analysis and Recognition, pp. 124-127, 1995.
[15] R. Manmatha and W.B. Croft, “Word Spotting: Indexing Handwritten Manuscripts,” Intelligent Multimedia Information Retrieval, Mark Maybury, ed., AAAI/MIT Press, April 1998.
[16] R. Manmatha, C. Han, and E.M. Riseman, “Word Spotting: A New Approach to Indexing Handwritting,” Proc. Computer Vision and Pattern Recognition Conf., pp. 53-62, 1996.
[17] R. Manmatha and N. Srimal, “Scale Space Technique for Word Segmentation in Handwritten Documents,” Scale-Space Theories in Computer Vision, pp. 22-33, 1999.
[18] D. Marr and E. Hildreth, “Theory of Edge Detection,” Proc. Royal Soc. London, vol. 207-B, pp. 187-217, 1980.
[19] U.V. Marti and H. Bunke, “A Full English Sentence Database for Off-Line Handwriting Recognition,” Proc. Fifth Int'l Conf. Document Analysis and Recognition, pp. 705-708, 1999.
[20] U.V. Marti and H. Bunke, “Text Line Segmentation and Word Recognition in a System for General Writer Independent Handwriting Recognition,” Proc. Sixth Int'l Conf. Document Analysis and Recognition, pp. 159-163, 2001.
[21] U.V. Marti and H. Bunke, “Using a Statistical Language Model to Improve the Performance of an HMM-Based Cursive Handwriting Recognition System,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 15, no. 1, pp. 65-90, 2001.
[22] G. Nagy, “Twenty Years of Document Image Analysis in PAMI,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 38-62, Jan. 2000.
[23] N. Otsu, “A Threshold Selection Method from Gray-Level Histogram,” IEEE Trans. Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979.
[24] R. Plamondon and S.N. Srihari, “Online and Offline Handwriting Recognition: A Comprehensive Survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, Jan. 2000.
[25] T.M. Rath, V. Lavrenko, and R. Manmatha, “A Search Engine for Historical Manuscript Images,” Proc. ACM Int'l Conf. Research and Development in Information Retrieval, pp. 369-376, 2004.
[26] G. Seni and E. Cohen, “External Word Segmentation of Off-Line Handwritten Text Lines,” Pattern Recognition, vol. 27, pp. 41-52, 1994.
[27] A.W. Senior and A.J. Robinson, “An Off-Line Cursive Handwriting Recognition System,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, pp. 309-321, 1998.
[28] C. Tomai, B. Zhang, and V. Govindaraju, “Transcript Mapping for Historic Handwritten Document Images,” Proc. Eighth Int'l Workshop Frontiers in Handwriting Recognition, pp. 413-418, 2002.
[29] O. Trier, A. Jain, and T. Taxt, “Feature Extraction Methods for Character Recognition— A Survey,” Pattern Recognition, vol. 29, pp. 641-662, 1996.
[30] A. Vinciarelli and J. Luettin, “Off-Line Cursive Script Recognition Based on Continuous Density HMM,” Proc. Seventh Int'l Workshop Frontiers in Handwriting Recognition, pp. 493-498, Sept. 2000.
[31] J.A. Weickert, S. Ishikawa, and A. Imiya, “On the History of Gaussian Scale-Space Axiomatics,” Gaussian Scale-Space Theory, J. Sporring, M. Nielsen, L.M.J. Florack, and P. Johansen, eds., pp. 45-59. Kluwer Academic, 1997.
Index Terms:
Index Terms- Segmentation, document and text processing, document analysis, handwriting analysis, document indexing, smoothing, optical character recognition.
Citation:
R. Manmatha, Jamie L. Rothfeder, "A Scale Space Approach for Automatically Segmenting Words from Historical Handwritten Documents," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1212-1225, Aug. 2005, doi:10.1109/TPAMI.2005.150
