Abstract
In handwritten text recognition, “sliding window” feature extraction represent the visual information contained in written text as feature vector sequences. In this paper, we explore the parameter space of feature weights in search for optimal weights and feature selection using the coordinate descent method. We report a gain of about 5% AUC performance. We use a public dataset for evaluation and also discuss the effects and limitations of “word pruning,” a technique in word spotting that is commonly used to boost performance and save computational time.
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References
Rath, T.M., Manmatha, R.: Word image matching using dynamic time warping. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-521–II-527 (2003)
Rath, T.M., Manmatha, R.: Features for Word Spotting in Historical Manuscripts. In: International Conference on Document Analysis and Recognition, pp. 218–222 (2003)
Manmatha, R., Croft, W.B.: Word Spotting: Indexing Handwritten Archives (1997)
Schwartz, R., LaPre, C., Makhoul, J., Raphael, C., Zhao, Y.: Language-independent ocr using a continuous speech recognition system. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 3, pp. 99–103 (1996)
Wienecke, M., Fink, G., Sagerer, G.: Toward automatic video-based whiteboard reading. International Journal of Document Analysis and Recognition (IJDAR) 7, 188–200 (2005)
Pltz, T., Fink, G.: Markov models for offline handwriting recognition: a survey. International Journal on Document Analysis and Recognition (IJDAR) 12, 269–298 (2009)
Marti, U.V., Bunke, H.: Using a statistical language model to improve the performance of an hmm-based cursive handwriting recognition system. International Journal of Pattern Recognition and Artificial Intelligence 15, 65–90 (2001)
Kane, S., Lehman, A., Partridge, E.: Indexing george washingtons handwritten manuscripts. Center for Intelligent Information Retrieval. Computer Science Department, University of Massachusetts, Amherst, MA 1003 (2001)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26, 43–49 (1978)
Bertsekas, D.P., Bertsekas, D.P.: Nonlinear Programming, 2nd edn. Athena Scientific (1999)
Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character hmms. Pattern Recogn. Lett. 33, 934–942 (2012)
Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145–1159 (1997)
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Wahlberg, F., Brun, A. (2013). Feature Weight Optimization and Pruning in Historical Text Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_10
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DOI: https://doi.org/10.1007/978-3-642-41939-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
eBook Packages: Computer ScienceComputer Science (R0)