BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts
This addresses the lack of datasets for layout analysis in non-Latin scripts, which is incremental as it builds on existing baseline detection methods.
The authors tackled the problem of text line retrieval in Arabic-script manuscripts by creating a public dataset of 400 annotated document images and proposing a fully convolutional encoder-decoder network for extraction, achieving unspecified results.
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.