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arXiv · 1907.04041

BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts

Benjamin Kiessling·Daniel Stökl Ben Ezra·Matthew Thomas Miller
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Abstract

The application of handwritten text recognition to historical works is highly dependant on accurate text line retrieval. A number of systems utilizing a robust baseline detection paradigm have emerged recently but the advancement of layout analysis methods for challenging scripts is held back by the lack of well-established datasets including works in non-Latin scripts. We present a dataset of 400 annotated document images from different domains and time periods. A short elaboration on the particular challenges posed by handwriting in Arabic script for layout analysis and subsequent processing steps is given. Lastly, we propose a method based on a fully convolutional encoder-decoder network to extract arbitrarily shaped text line images from manuscripts.

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Benjamin Kiessling, Daniel Stökl Ben Ezra, Matthew Thomas Miller. 2019-07-09. BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts. https://arxiv.org/abs/1907.04041

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