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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tokahashi, H. Griffin, T.D. |
| Copyright Year | 1993 |
| Description | Author affiliation: IBM Almaden Res. Center, San Jose, CA, USA (Tokahashi, H.; Griffin, T.D.) |
| Abstract | The recent significant enhancement of OCR systems recognition rates has been driven mainly by combining different feature sets or by adopting a voting scheme using multiple independent algorithms. Voting is effective but computationally expensive. A general framework for economical enhancement of recognition rate that focuses on a critical reordering of a few top candidates is described. After the execution of the base OCR algorithm (a three-layer neural network), a linear tournament verification is executed using one-to-one small network verifiers to improve the ordering of the top candidates. Thirty-four one-to-one verifiers were developed for the uppercase English alphabet. Fourteen of these use special features; however, the rest use the same features as those in the base algorithm. On the NIST uppercase data set, the recognition rate for the new system is 95.8%, showing a 1.2% improvement over the system without verification. Although the improvement is modest, the costs in both efficiency and development effort are small.< |
| Starting Page | 585 |
| Ending Page | 588 |
| File Size | 368083 |
| Page Count | 4 |
| File Format | |
| ISBN | 0818649607 |
| DOI | 10.1109/ICDAR.1993.395667 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1993-10-20 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Optical character recognition software Costs NIST Voting Neural networks Feature extraction Ores Large-scale systems Robustness Topology |
| Content Type | Text |
| Resource Type | Article |
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