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| Content Provider | ACM Digital Library |
|---|---|
| Author | Borsack, Julie Condit, Allen Taghva, Kazem |
| Copyright Year | 1996 |
| Abstract | We give a comprehensive report on our experiments with retrieval from OCR-generated text using systems based on standard models of retrieval. More specifically, we show that average precision and recall is not affected by OCR errors across systems for several collections. The collections used in these experiments include both actual OCR-generated text and standard information retrieval collections corrupted through the simulation of OCR errors. Both the actual and simulation experiments include full-text and abstract-length documents. We also demonstrate that the ranking and feedback methods associated with these models are generally not robust enough to deal with OCR errors. It is further shown that the OCR errors and garbage strings generated from the mistranslation of graphic objects increase the size of the index by a wide margin. We not only point out problems that can arise from applying OCR text within an information retrieval environment, we also suggest solutions to overcome some of these problems. |
| Starting Page | 64 |
| Ending Page | 93 |
| Page Count | 30 |
| File Format | |
| ISSN | 10468188 |
| e-ISSN | 15582868 |
| DOI | 10.1145/214174.214180 |
| Volume Number | 14 |
| Issue Number | 1 |
| Journal | ACM Transactions on Information Systems (TOIS) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1996-01-11 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Error correction Feedback Optical character recognition Ranking algorithms |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Information Systems Business, Management and Accounting |
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