Extending Page Segmentation Algorithms for Mixed-Layout Document Processing

Citation data:

2011 International Conference on Document Analysis and Recognition, ISSN: 1520-5363, Page: 1245-1249

Publication Year:
Usage 248
Downloads 208
Abstract Views 40
Captures 18
Readers 18
Citations 7
Citation Indexes 7
Repository URL:
Amy Winder; Tim Andersen; Elisa H. Barney Smith
Institute of Electrical and Electronics Engineers (IEEE); IEEE
Computer Science; page segmentation; RAST; Voronoi; open source OCR; Computer Sciences
conference paper description
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.