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Segmentation of text lines using multi-scale CNN from warped printed and handwritten document images

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Abstract

Paper documents are ideal sources of useful information and have a profound impact on every aspect of human lives. These documents may be printed or handwritten and contain information as combinations of texts, figures, tables, charts, etc. This paper proposes a method to segment text lines from both flatbed scanned/camera-captured heavily warped printed and handwritten documents. This work uses the concept of semantic segmentation with the help of a multi-scale convolutional neural network. The results of line segmentation using the proposed method outperform a number of similar proposals already reported in the literature. The performance and efficacy of the proposed method have been corroborated by the test result on a variety of publicly available datasets, including ICDAR, Alireza, IUPR, cBAD, Tobacco-800, IAM, and our dataset.

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Correspondence to Arpita Dutta.

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Dutta, A., Garai, A., Biswas, S. et al. Segmentation of text lines using multi-scale CNN from warped printed and handwritten document images. IJDAR 24, 299–313 (2021). https://doi.org/10.1007/s10032-021-00370-8

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