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


  • Wenzhen Zhu Amazon Web Services
  • Negin Sokhandan Amazon Web Services
  • Guang Yang Amazon Web Services
  • Sujitha Martin Amazon Web Services
  • Suchitra Sathyanarayana Amazon Web Services



Document Segmentation, OCR, Document Layout Analysis, Historical Document Processing


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




How to Cite

Zhu, W., Sokhandan, N., Yang, G., Martin, S., & Sathyanarayana, S. (2022). DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12643-12649.