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Line and word segmentation of handwritten text document by mid-point detection and gap trailing

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Abstract

This paper presents the text line and word segmentation from unconstrained handwritten documents based on horizontal projection histogram (HPH) to detect mid-points and gap trailing between lines. The midpoints are estimated from the HPH for the first 100 to 200 columns of the whole document. Then, considering the mid-points, the gap is tracked between two consecutive lines from locally computed HPH for a block having k rows and j columns. The HPH block is examined for various cases to locate optimal rows that separate adjacent lines. The proposed method segments curve, touching and skew-lines and is robust to writing variation and language independent. Word segmentation is not treated as a separate problem and goes efficiently alongside the line segmentation. As the trailing of space between neighboring lines goes on, the vertical projection Histogram (VPH) of t columns is monitored between the above and below separator of a line and find the optimal word separator. The algorithm is evaluated on two isolated datasets of different languages (Meitei Mayek and English). Text-line and word segmentation on Meitei Mayek handwritten documents achieve 91.84% and 88.96% accuracy respectively. Similarly, the handwritten English document meets 94.18% and 87.73% accuracy for line and word segmentation.

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Authors and Affiliations

  1. NIT Manipur, Imphal, India

    Inunganbi Sanasam & Khumanthem Manglem Singh

  2. NIT Hamirpur, Imphal, HP, India

    Prakash Choudhary

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  1. Inunganbi Sanasam
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  2. Prakash Choudhary
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  3. Khumanthem Manglem Singh
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Correspondence to Inunganbi Sanasam.

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Sanasam, I., Choudhary, P. & Singh, K.M. Line and word segmentation of handwritten text document by mid-point detection and gap trailing. Multimed Tools Appl 79, 30135–30150 (2020). https://doi.org/10.1007/s11042-020-09416-1

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  • DOI: https://doi.org/10.1007/s11042-020-09416-1

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