Name
Abstract | ||
|---|---|---|
This paper presents BLSTM-CTC (bidirectional LSTM-Connectionist Temporal Classification), a novel scheme to tackle the Chinese image text recognition problem. Different from traditional methods that perform the recognition on the single character level, the input of BLSTM-CTC is an image text composed of a line of characters and the output is a recognized text sequence, where the recognition is carried out on the whole image text level. To train a neural network for this challenging task, we collect over 2 million news titles from which we generate over 1 million noisy image texts, covering almost the vast majority of common Chinese characters. With these training data, a RNN training procedure is conducted to learn the recognizer. We also carry out some adaptations on the neural network to make it suitable for real scenarios. Experiments on text images from 13 TV channels demonstrate the effectiveness of the proposed pipeline. The results all outperform those of a baseline system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-981-10-3005-5_43 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Chinese image text recognition,BLSTM,CTC,Segmentation-free | Training set,Chinese characters,Pattern recognition,Computer science,Segmentation,Communication channel,Artificial intelligence,Single character,Baseline system,Artificial neural network,Text recognition | Conference |
Volume | ISSN | Citations |
663 | 1865-0929 | 1 |
PageRank | References | Authors |
0.37 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuanlei Zhai | 1 | 1 | 0.37 |
| Zhineng Chen | 2 | 192 | 25.29 |
| J.X. Li | 3 | 403 | 113.63 |
| Bo Xu | 4 | 112 | 20.58 |