The goal of this work is to add the capability to segment documents containing text, graphics, and pictures in the open source OCR engine OCRopus. To achieve this goal, OCRopus' RAST algorithm was improved to recognize non-text regions so that mixed content documents could be analyzed in addition to text-only documents. Also, a method for classifying text and non-text regions was developed and implemented for the Voronoi algorithm enabling users to perform OCR on documents processed by this method. Finally, both algorithms were modified to perform at a range of resolutions. Our testing showed an improvement of 15-40% for the RAST algorithm, giving it an average segmentation accuracy of about 80%. The Voronoi algorithm averaged around 70% accuracy on our test data. Depending on the particular layout and idiosyncracies of the documents to be digitized, however, either algorithm could be sufficiently accurate to be utilized.
© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. DOI: 10.1109/ICDAR.2011.251
Amy Winder, Tim Andersen and Elisa Barney Smith. "Extending Page Segmentation Algorithms for Mixed-Layout Document Processing" 11th International Conference on Document Analysis and Recognition (ICDAR 2011)
Available at: http://works.bepress.com/elisa_barney_smith/76/