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| Content Provider | ACM Digital Library |
|---|---|
| Author | Rahnemoonfar, Maryam Plale, Beth |
| Abstract | Geometric distortions are among the major challenging issues in the analysis of historical document images. Such distortions appear as arbitrary warping, folds and page curl, and have detrimental effects upon recognition (OCR) and readability. While there are many dewarping techniques discussed in the literature, there exists no standard method by which their performance can be evaluated against each other. In particular, there is not any satisfactory method capable of comparing the results of existing dewarping techniques on arbitrary wrapped documents. The existing methods either rely on the visual comparison of the output and input images or depend on the recognition rate of an OCR system. In the case of historical documents, OCR either is not available or does not generate an acceptable result. In this paper, an objective and automatic evaluation methodology for document image dewarping technique is presented. In the first step, all the baselines in the original distorted image as well as dewarped image are modelled precisely and automatically. Then based on the mathematical function of each line, a comprehensive metric which calculates the performance of a dewarping technique is introduced. The presented method does not require user interference in any stage of evaluation and therefore is quite objective. Experimental results, applied to two state-of-the art dewarping methods and an industry-standard commercial system, demonstrate the effectiveness of the proposed dewarping evaluation method. |
| Starting Page | 331 |
| Ending Page | 334 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450320771 |
| DOI | 10.1145/2467696.2467744 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-07-22 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Large scale digitization Performance evaluation Arbitrary warping |
| Content Type | Text |
| Resource Type | Article |
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