Name
Abstract | ||
|---|---|---|
How to read Uyghur text from biomedical graphic images is a challenge problem due to the complex layout and cursive writing of Uyghur. In this paper, we propose a system that extracts text from Uyghur biomedical images, and matches the text in a specific lexicon for semantic analysis. The proposed system possesses following distinctive properties: first, it is an integrated system which firstly detects and crops the Uyghur text lines using a single fully convolutional neural network, and then keywords in the lexicon are matched by a well-designed matching network. Second, to train the matching network effectively an online sampling method is applied, which generates synthetic data continually. Finally, we propose a GPU acceleration scheme for matching network to match a complete Uyghur text line directly rather than a single window. Experimental results on benchmark dataset show our method achieves a good performance of F-measure 74.5%. Besides, our system keeps high efficiency with 0.5s running time for each image due to the GPU acceleration scheme. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s12021-017-9350-0 | Neuroinformatics |
Keywords | Field | DocType |
Text detection,Text extracting,Text recognition,Uyghur | Computer vision,Pattern recognition,Computer science,Convolutional neural network,Lexicon,Synthetic data,Acceleration,Sampling (statistics),Artificial intelligence,Cursive Writing,Text detection,Text recognition | Journal |
Volume | Issue | ISSN |
16 | 3-4 | 1539-2791 |
Citations | PageRank | References |
3 | 0.38 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shancheng Fang | 1 | 25 | 4.43 |
| Hongtao Xie | 2 | 439 | 47.79 |
| Zhineng Chen | 3 | 192 | 25.29 |
| Yizhi Liu | 4 | 117 | 10.83 |
| Yan Li | 5 | 399 | 95.68 |