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Segmentation and recognition system for unknown-length handwritten digit strings

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Abstract

The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-length handwritten digit strings by combining several explicit segmentation methods depending on the configuration link between digits. Three segmentation methods are combined based on histogram of the vertical projection, the contour analysis and the sliding window Radon transform. A recognition and verification module based on support vector machine classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Moreover, various submodules are included leading to enhance the robustness of the proposed system. Experimental results conducted on the benchmark dataset show that the proposed system is effective for segmenting handwritten digit strings without prior knowledge of their length comparatively to the state of the art.

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Authors and Affiliations

  1. Ecole Nationale Supérieure d’Informatique (ESI), BP 68M, 16309, OUED SMAR, El-Harrach, Algiers, Algeria

    Abdeljalil Gattal

  2. LAMIS Laboratory, Larbi Tebessi University, Route de Constantine, 12002, Tébessa, Algeria

    Abdeljalil Gattal

  3. LISIC Lab. , Faculty of Electronics and Computer Science, University of Science and Technology Houari Boumédienne (USTHB), PO Box 32, 16111, Bab-Ezzouar, El-Alia, Algiers, Algeria

    Youcef Chibani & Bilal Hadjadji

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  1. Abdeljalil Gattal
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  2. Youcef Chibani
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  3. Bilal Hadjadji
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Correspondence to Abdeljalil Gattal.

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Gattal, A., Chibani, Y. & Hadjadji, B. Segmentation and recognition system for unknown-length handwritten digit strings. Pattern Anal Applic 20, 307–323 (2017). https://doi.org/10.1007/s10044-017-0607-x

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