The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.11 - November (2008 vol.30)
pp: 1945-1957
Tijn van der Zant , University of Groningen, Groningen
Lambert Schomaker , University of Groningen, Groningen
Koen Haak , University of Groningen, Groningen
ABSTRACT
For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.
INDEX TERMS
Handwriting analysis, Interactive systems, Image/video retrieval, Computer vision, Computational neuroscience, Digital Libraries, Information Storage and Retrieval, Information Technology and Systems, Invariants, Feature Measurement, Image Processing and Computer Vision, Computing Methodologies
CITATION
Tijn van der Zant, Lambert Schomaker, Koen Haak, "Handwritten-Word Spotting Using Biologically Inspired Features", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 11, pp. 1945-1957, November 2008, doi:10.1109/TPAMI.2008.144
REFERENCES
[1] A. Vailaya, M. Figueiredo, A. Jain, and H. Zhang, “Image Classification for Content-Based Indexing,” IEEE Trans. Image Processing, vol. 10, no. 1, pp. 117-130, 2001.
[2] H.-S. Park and S.-W. Lee, “Hidden Markov Mesh Random Field: Theory and Its Application to Handwritten Character Recognition,” Proc. Third Int'l Conf. Document Analysis and Recognition, vol. 1, p. 409, 1995.
[3] V. Lavrenko, T.M. Rath, and R. Manmatha, “Holistic Word Recognition for Handwritten Historical Documents,” Proc. Int'l Workshop Document Image Analysis for Libraries, pp. 278-287, Jan. 2004.
[4] M. Liwicki and H. Bunke, “HMM-Based On-Line Recognition of Handwritten Whiteboard Notes,” Proc. 10th Int'l Workshop Frontiers in Handwriting Recognition, pp. 595-599, Oct. 2006.
[5] U. Pal, K. Roy, and F. Kimura, “A Lexicon Driven Method for Unconstrained Bangla Handwritten Word Recognition,” Proc. 10th Int'l Workshop Frontiers in Handwriting Recognition, pp. 601-606, Oct. 2006.
[6] E. Caillault and C. Viard-Gaudin, “Using Segmentation Constraints in an Implicit Scheme for On-Line Word Recognition,” Proc. 10th Int'l Workshop Frontiers in Handwriting Recognition, pp.607-612, Oct. 2006.
[7] L. Schomaker, E. de Leau, and L. Vuurpijl, “Using Pen-Based Outlines for Object-Based Annotation and Image-Based Queries,” Proc. Third Int'l Conf. Visual Information and Information Systems, pp. 585-592, 1999.
[8] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[9] F. Lauer, C.Y. Suen, and G. Bloch, “A Trainable Feature Extractor for Handwritten Digit Recognition,” Pattern Recognition, vol. 40, no. 6, pp. 1816-1824, 2007.
[10] T. Rath, R. Manmatha, and V. Lavrenko, “A Search Engine for Historical Manuscript Images,” Proc. ACM SIGIR '04, pp. 369-376, July 2004.
[11] L. Schomaker, K. Franke, and M. Bulacu, “Using Codebooks of Fragmented Connected-Component Contours in Forensic and Historic Writer Identification,” Pattern Recognition Letters, vol. 28, no. 6, pp. 719-727, 2007.
[12] L. Schomaker, “Word Mining in a Sparsely-Labeled Handwritten Collection,” Proc. Conf. Recognition and Retrieval, IS&T/SPIE Int'l Symp. Electronic Imaging, 2008.
[13] M. Bulacu, R. van Koert, L. Schomaker, and T. van der Zant, “Layout Analysis of Handwritten Historical Documents for Searching the Archive of the Cabinet of the Dutch Queen,” Proc. Ninth Int'l Conf. Document Analysis and Recognition, 2007.
[14] T. van der Zant, “Large Scale Parallel Document Image Processing,” Proc. Int'l Conf. Document Recognition and Retrieval, 2008.
[15] L. Schomaker, “Retrieval of Handwritten Lines in Historical Documents,” Proc. Int'l Conf. Document Analysis and Recognition, 2007.
[16] L. Schomaker, “Word Mining in a Sparsely-Labeled Handwritten Collection,” Proc. Int'l Conf. Document Recognition and Retrieval, 2008.
[17] T. Joachims, “A Support Vector Method for Multivariate Performance Measures,” Proc. Int'l Conf. Machine Learning, 2005.
[18] T. Joachims, “Training Linear SVMS in Linear Time,” Proc. ACM Conf. Knowledge Discovery and Data Mining, 2006.
[19] T. Serre et al., “Robust Object Recognition with Cortex-Like Mechanisms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 411-426, Mar. 2007.
[20] T. Poggio and S. Edelman, “A Network That Learns to Recognize Three-Dimensional Objects,” Nature, vol. 343, pp. 263-266, 1990.
[21] T. Serre, L. Wolf, and T. Poggio, “Object Recognition with Features Inspired by Visual Cortex,” Proc. Computer Vision and Pattern Recognition, June 2005.
[22] T. Serre et al., “A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Visual Stream in Primate Visual Cortex,” aI Memo 2005-036/CBCL Memo 259, Massachusetts Inst. of Tech nology, 2005.
[23] P. Xiu and H.S. Baird, “Whole-Book Recognition Using Mutual-Entropy-Driven Model Adaptation,” Document Recognition and Retrieval XV, Proc. Soc. of Photo-Optical Instrumentation Engineers Conf., B.A. Yanikoglu and K. Berkner, eds., vol. 6815, pp.681506-681506-10, Jan. 2008.
[24] P. Sarkar and G. Nagy, “Style Consistent Classification of Isogenous Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 88-98, Jan. 2005.
[25] L. Rabiner and B. Juang, “An Introduction to Hidden Markov Models,” IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, 1986.
[26] M. Goodale and A. Milner, “Separate Visual Pathways for Perception and Action,” Trends in Neuroscience, vol. 15, pp. 20-25, 1992.
[27] L. Ungerleider and M. Mishkin, “Two Cortical Visual Systems,” The Analysis of Visual Behavior. MIT Press, 1982.
[28] M. Riesenhuber, and T. Poggio, “Hierarchical Models of Object Recognition in Cortex,” Nature Neuroscience, vol. 2, no. 11, pp.1019-1025, 1999.
[29] D. Hubel and T. Wiesel, “Receptive Fields, Binocular Interaction, and Functional Architecture of the Cat's Visual Cortex,” J.Psychology, vol. 160, pp. 106-154, 1962.
[30] B. Heisele et al., “Categorization by Learning and Combining Object Parts,” Proc. Neural Information Processing Systems, pp. 1239-1245, 2001.
[31] M. Weber, W. Welling, and P. Perona, “Unsupervised Learning If Models of Recognition,” Proc. European Conf. Computer Vision, pp.1001-1108, 2000.
[32] A. Mohan, C. Papageorgiou, and T. Poggio, “Example-Based Object Detection in Images by Components,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, pp. 349-361, 2001.
[33] S. Ullman, M. Vidal-Naquet, and E. Sali, “Visual Features of Intermediate Complexity and Their Use in Classification,” Nature Neuroscience, vol. 5, no. 7, pp. 682-687, 2002.
[34] K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors,” Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 257-263, 2003.
[35] M. Powell, “Radial Basis Functions for Multivariable Interpolation: A Review,” Algorithms for Approximation, pp. 143-167, 1987.
[36] J. Jones and L. Palmer, “An Evaluation of the Two Dimensional Gabor Filter Model of Simple Receptive Fields in Cat Striate Cortex,” J. Neurophysiology, vol. 58, pp. 1233-1258, 1987.
[37] D. Gabor, “Theory of Communication,” J. IEE, vol. 93, pp. 429-459, 1946.
[38] T. Poggio and E. Bizzi, “Generalization in Vision and Motor Control,” Nature, vol. 431, pp. 768-774, 2004.
[39] C. Bishop, Neural Networks for Pattern Recognition. Oxford Univ. Press, 1995.
[40] S. Zinger, J. Nerbonne, L. Schomaker, and H. van Schie, “Content-Based Text Line Comparison for Historical Document Retrieval,” Proc. Computational Phonology Workshop, Recent Advances in natural Language Processing Conf., pp. 79-84, Sept. 2007.
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool