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A Nom historical document recognition system for digital archiving

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Abstract

A Nom historical document recognition system is being developed for digital archiving that uses image binarization, character segmentation, and character recognition. It incorporates two versions of off-line character recognition: one for automatic recognition of scanned and segmented character patterns (7660 categories) and the other for user handwritten input (32,695 categories). This separation is used since including less frequently appearing categories in automatic recognition increases the misrecognition rate without reliable statistics on the Nom language. Moreover, a user must be able to check the results and identify the correct categories from an extended set of categories, and a user can input characters by hand. Both versions use the same recognition method, but they are trained using different sets of training patterns. Recursive X–Y cut and Voronoi diagrams are used for segmentation; k–d tree and generalized learning vector quantization are used for coarse classification; and the modified quadratic discriminant function is used for fine classification. The system provides an interface through which a user can check the results, change binarization methods, rectify segmentation, and input correct character categories by hand. Evaluation done using a limited number of Nom historical documents after providing ground truths for them showed that the two stages of recognition along with user checking and correction improved the recognition results significantly.

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Acknowledgments

We thank the National Library of Vietnam and the Vietnamese Nom Preservation Foundation for providing the Nom historical document pages. This research is being supported by Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JSPS) (contract numbers (B) 24300095 and (S) 25220401).

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan

    Truyen Van Phan, Kha Cong Nguyen & Masaki Nakagawa

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  1. Truyen Van Phan
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  2. Kha Cong Nguyen
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  3. Masaki Nakagawa
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Correspondence to Masaki Nakagawa.

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Van Phan, T., Cong Nguyen, K. & Nakagawa, M. A Nom historical document recognition system for digital archiving. IJDAR 19, 49–64 (2016). https://doi.org/10.1007/s10032-015-0257-8

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