Text line segmentation from struck-out handwritten document images

Shivakumara, Palaiahnakote and Jain, Tanmay and Pal, Umapada and Surana, Nitish and Antonacopoulos, Apostolos and Lu, Tong (2022) Text line segmentation from struck-out handwritten document images. Expert Systems with Applications, 210. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2022.118266.

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In the case of freestyle everyday handwritten documents, writing, erasing, striking out, and overwriting are common behaviors of the writers. This not cleanly-written text poses significant challenges for text line seg-mentation. Accurate text line segmentation in handwritten documents is essential to the success of several real -world applications, such as answer script evaluation, fraud document identification, writer identification, document age estimation and writer gender classification, to name a few. This paper proposes the first, to the authors' best knowledge, text line segmentation approach that is applicable in the presence of both cleanly -written and struck-out text. The approach consists of three steps. In the first step, components -at the word level -are detected in the input handwritten document images (containing both cleanly-written and struck-out text) based on stroke width information estimation, filtering of noise, and morphological operations. In the second step, the struck-out components are identified using the DenseNet deep learning model and treated differently to clean text in further analysis. In the third step, geometrical spatial features, the direction between candidate components and the overall text line, and the common overlapping region between adjacent com-ponents are evaluated to progressively form text lines. To evaluate the proposed steps and compare the proposed method to the state-of-the-art, experiments have been conducted on a new problem-focused dataset containing instances of struck-out text in handwritten documents, as well as on two standard datasets (ICDAR2013 text line segmentation contest dataset and ICDAR2019 HDRC dataset) to show the proposed steps are effective and useful, with superior performance compared to existing methods.

Item Type: Article
Funders: Universiti Malaya [GPF096B-2020] [GPF096C-2020] [GPF096A-2020]
Uncontrolled Keywords: Handwriting recognition; Writer identification; Connected component analysis; Deep learning; Struck -out words; Text line segmentation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 25 Aug 2023 02:59
Last Modified: 25 Aug 2023 02:59
URI: http://eprints.um.edu.my/id/eprint/40919

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