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| Content Provider | Springer Nature Link |
|---|---|
| Author | Lopresti, Daniel |
| Copyright Year | 2009 |
| Abstract | Errors are unavoidable in advanced computer vision applications such as optical character recognition, and the noise induced by these errors presents a serious challenge to downstream processes that attempt to make use of such data. In this paper, we apply a new paradigm we have proposed for measuring the impact of recognition errors on the stages of a standard text analysis pipeline: sentence boundary detection, tokenization, and part-of-speech tagging. Our methodology formulates error classification as an optimization problem solvable using a hierarchical dynamic programming approach. Errors and their cascading effects are isolated and analyzed as they travel through the pipeline. We present experimental results based on a large collection of scanned pages to study the varying impact depending on the nature of the error and the character(s) involved. This dataset has also been made available online to encourage future investigations. |
| Starting Page | 141 |
| Ending Page | 151 |
| Page Count | 11 |
| File Format | |
| ISSN | 14332833 |
| Journal | International Journal of Document Analysis and Recognition (IJDAR) |
| Volume Number | 12 |
| Issue Number | 3 |
| e-ISSN | 14332825 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2009-09-25 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Performance evaluation Optical character recognition Sentence boundary detection Tokenization Part-of-speech tagging Pattern Recognition Image Processing and Computer Vision |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Computer Vision and Pattern Recognition Software |
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