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Content Provider | IEEE Xplore Digital Library |
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Author | Winder, A. Andersen, T. Smith, E.H.B. |
Copyright Year | 2011 |
Abstract | 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. |
Starting Page | 1245 |
Ending Page | 1249 |
File Size | 614661 |
Page Count | 5 |
File Format | |
ISBN | 9781457713507 |
ISSN | 15205363 |
e-ISBN | 9780769545202 |
DOI | 10.1109/ICDAR.2011.251 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-09-18 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Image segmentation Classification algorithms Image resolution Algorithm design and analysis Layout Merging open source OCR page segmentation RAST Voronoi |
Content Type | Text |
Resource Type | Article |
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