Name
Title | ||
|---|---|---|
A Fast and Accurate Fully Convolutional Network for End-to-End Handwritten Chinese Text Segmentation and Recognition |
Abstract | ||
|---|---|---|
Handwritten Chinese Text Recognition (HCTR) is a challenging problem due to its high complexity. Previous methods based on over-segmentation, hidden Markov model (HMM) or long short-term memory recurrent neural network (LSTM-RNN) have achieved great success in recognition results. However, all of them, including over-segmentation based methods, are incompetent in accurate segmentation of single character. To solve this problem, we propose a fast and accurate fully convolutional network for end-to-end segmentation and recognition of handwritten Chinese text. Experiments on CASIA-HWDB datasets and ICDAR 2013 competition dataset show that our method achieves a competitive performance on recognition and produces great character segmentation results. Moreover, our model reaches a real-time speed of 70 fps, which is fast enough for various applications. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00014 | 2019 International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | Field | DocType |
handwritten Chinese text recognition,end-to-end segmentation and recognition,fully convolutional network | Pattern recognition,Computer science,End-to-end principle,Segmentation,Recurrent neural network,Text segmentation,Single character,Artificial intelligence,Hidden Markov model,Text recognition | Conference |
ISSN | ISBN | Citations |
1520-5363 | 978-1-7281-3015-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dezhi Peng | 1 | 1 | 3.05 |
| Lianwen Jin | 2 | 1337 | 113.14 |
| Yaqiang Wu | 3 | 5 | 2.75 |
| Zhepeng Wang | 4 | 6 | 5.08 |
| Mingxiang Cai | 5 | 1 | 1.70 |