Name
Title | ||
|---|---|---|
Modified Bootstrap Approach with State Number Optimization for Hidden Markov Model Estimation in Small-Size Printed Arabic Text Line Recognition. |
Abstract | ||
|---|---|---|
In printed Arabic text line recognition, hidden Markov model brings a facility from no pre-segmentation but leaves a hard work to model estimation. Although bootstrap training can supply good initialization, the bad image quality of small-size samples may make it difficult to find accurate model boundary. This paper introduces a modified bootstrap approach with state number optimization to improve the accuracy of model estimation. Experiments on small-size samples from the APTI dataset show that the modified bootstrap approach in this paper can decrease 13.3% error rate of word recognition and 14% error rate of character recognition than the original one. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-08979-9_33 | Lecture Notes in Artificial Intelligence |
Keywords | DocType | Volume |
Hidden Markov model,Optical character recognition,Model estimation,Bootstrap approach,State number optimization | Conference | 8556 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwei Jiang | 1 | 41 | 6.41 |
| Xiaoqing Ding | 2 | 1219 | 108.02 |
| Liangrui Peng | 3 | 80 | 17.67 |
| Changsong Liu | 4 | 358 | 36.20 |