Abstract
This paper deals with one of the new emerging multimedia data types, namely, handwritten cursive text. The paper presents two indexing methods for searching a collection of cursive handwriting. The first index, word-level index, treats word as pictogram and uses global features for retrieval. The word-level index is suitable for large collection of cursive text. While the second one, called stroke-level index, treats the word as a set of strokes. The stroke-level index is more accurate, but more costly than the word level index. Each word (or stroke) can be described with a set of features and, thus, can be stored as points in the feature space. The Karhunen-Loeve transform is then used to minimize the number of features used (data dimensionality) and thus the index size. Feature vectors are stored in an R-tree. We implemented both indexes and carried many simulation experiments to measure the effectiveness and the cost of the search algorithm. The proposed indexes achieve substantial saving in the search time over the sequential search. Moreover, the proposed indexes improve the matching rate up to 46% over the sequential search.
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References
Beckmann, N., et al.: The R*-tree: an efficient and robust access method for points and rectangles. In: Proc. of ACM SIGMOD (1990)
Jawahar, C.V., Balasubramanian, A., Meshesha, M., Namboodiri, A.: Retrieval of online handwriting by synthesis and matching. Pattern Recognition 42(7) (2009)
Roussopoulos, N., Leifker, D.: Direct spatial search on pictorial databases using packed r-trees. In: ACM SIGMOD, pp. 17–31 (1985)
Wang, J., Wu, C., Xu, Y., Shum, H.: Combining shape and physical models for on-line cursive handwriting synthesis. International Journal of Document Analysis and Recognition 7(4), 219–227 (2005)
Gersha, A., Gray, R.: Vector Quantization and Signal Compression. Kluwer Academic, Dordrecht (1992)
Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proc. of ACM SIGMOD (1984)
Jain, A., Namboodiri, A.: Indexing and Retrieval of On-line Handwritten Documents. In: Proc. of the 7th International Conference on Document Analysis and Recognition, p. 655 (2003)
Oda, H., Kitadai, A., Onuma, M., Nakagawa, M.: A Search Method for On-Line Handwritten Text Employing Writing-Box-Free Handwriting Recognition. In: Proc. of the 9th International Workshop on Frontiers in Handwriting Recognition, pp. 545–550 (2004)
Kamel, I., Faloutsos, C.: On packing Rtrees. In: Proc. of CIKM (1993)
Kamel, I., Faloutsos, C.: Hilbert R-tree: an improved r-tree using fractals. In: VLDB (1994)
Zheng, Y., Doermann, D.: Handwriting Matching and Its Application to Handwriting Synthesis. In: Proceedings of the 8th International Conference on Document Analysis and Recognition, pp. 861–865 (2005)
Ma, Y., Zhang, C.: Retrieval of cursive Chinese handwritten annotations based on radical model United States Patent 6681044 (2004)
Lopresti, D., Tomkins, A.: Pictographic naming. In: Tech. Rep. MITL- TR-21-92, Matsushita Information Technology Lab
Lopresti, D., Tomkins, A.: On the searchability of electronic ink. In: Tech. Rep. MITL- TR-114-94, Matsushita Information Technology Lab
Rubine, D.: The automatic recognition of gestures. In: PhD thesis, Carnegie Mellon University (1991)
Gatos, B., Pratikakis, I., Perantonis, S.J.: Hybrid Off-Line Cursive Handwriting Word Recognition. Pattern Recognition 2, 998–1002 (2006)
Al Aghbari, Z., Brook, S.: HAH manuscripts: A holistic paradigm for classifying and retrieving historical Arabic handwritten documents. Journal of Expert Systems with Applications (2009)
Wagner, R., Fisher, M.: The string-to-string correction problem. Journal of ACM 21, 168–173 (1974)
Varga, T., Bunke, H.: Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System. In: Proc. of the 7th International Conference on Document Analysis and Recognition, p. 618 (2003)
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Kamel, I. (2009). Efficient Index for Handwritten Text. In: Ślęzak, D., Grosky, W.I., Pissinou, N., Shih, T.K., Kim, Th., Kang, BH. (eds) Multimedia, Computer Graphics and Broadcasting. MulGraB 2009. Communications in Computer and Information Science, vol 60. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10512-8_4
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DOI: https://doi.org/10.1007/978-3-642-10512-8_4
Publisher Name: Springer, Berlin, Heidelberg
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