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Extending Page Segmentation Algorithms for Mixed-Layout Document Processing

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3 Author(s)
Amy Winder ; Comput. Sci. Dept., Boise State Univ., Boise, ID, USA ; Tim Andersen ; Elisa H. Barney Smith

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.

Published in:

2011 International Conference on Document Analysis and Recognition

Date of Conference:

18-21 Sept. 2011