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Digitization of newspapers is of interest for many reasons includingpreservation of history, accessibility and search ability, etc. Whiledigitization of documents such as scientific articles and magazines isprevalent in literature, one of the main challenges for digitization ofnewspaper lies in its complex layout (e.g. articles spanning multiple columns,text interrupted by images) analysis, which is necessary to preserve humanread-order. This work provides a major breakthrough in the digitization ofnewspapers on three fronts: first, releasing a dataset of 3000 fully-annotated,real-world newspaper images from 21 different U.S. states representing anextensive variety of complex layouts for document layout analysis; second,proposing layout segmentation as a precursor to existing optical characterrecognition (OCR) engines, where multiple state-of-the-art image segmentationmodels and several post-processing methods are explored for document layoutsegmentation; third, providing a thorough and structured evaluation protocolfor isolated layout segmentation and end-to-end OCR.

DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts