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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sui-Yu Wang Baird, H. Chang An |
| Copyright Year | 2009 |
| Abstract | We report an automatic feature discovery method that achieves results comparable to a manually chosen, larger feature set on a document image content extraction problem: the location and segmentation of regions containing handwriting and machine-printed text in documents images. This approach is a greedy forward selection algorithm that iteratively constructs one linear feature at a time. The algorithm finds error clusters in the current feature space, then projects one tight cluster into the null space of the feature mapping, where a new feature that helps to classify these errors can be discovered. We conducted experiments on 87 diverse test images. Four manually chosen linear features with an error rate of 16.2% were given to the algorithm; the algorithm then found an additional ten features; the composite 14 features achieve an error rate of 13.8%. This outperforms a feature set of size 14 chosen by Principal Component Analysis (PCA) with an error rate of 15.4%. It also nearly matches the error rate of 13.6% achieved by twice as many manually chosen features. Thus our algorithm appears to compete with both the widely used PCA method and tedious and expensive trial-and-error manual exploration. |
| Starting Page | 1076 |
| Ending Page | 1080 |
| File Size | 3327123 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424445004 |
| ISSN | 15205363 |
| DOI | 10.1109/ICDAR.2009.198 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-26 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Error analysis Principal component analysis Clustering algorithms Null space Testing Text analysis Iterative algorithms Filters Image analysis Handwriting recognition |
| Content Type | Text |
| Resource Type | Article |
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