Name
Abstract | ||
|---|---|---|
Handwritten Chinese character recognition (HCCR) is an important research field of pattern recognition, which has attracted extensive studies during the past decades. Recently convolutional neural network (CNN) based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition. Nevertheless, handwritten Chinese character recognition is still limited to be effectively used in the actual environment due to the large-scale vocabulary and great diversity of handwriting style. In this paper, we constructed a handwritten Chinese character recognition service based on convolutional neural network, which tries to make effective use of handwritten based printed fonts and existing handwritten database. At the same time, the service can effectively collect more handwritten data to expand the training dataset, which makes it easy to adapt to the new handwriting styles. Meanwhile, We propose a multi-level recognition theory applied to online handwritten Chinese character recognition, which may improve the accuracy of handwritten Chinese character recognition and break the limitations of handwritten Chinese character recognition by identifying the structure of Chinese characters and possible stroke orders firstly. Furthermore, we try to apply the method of online character recognition to the offline character recognition based on the basic writing rules. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICWS.2018.00010 | 2018 IEEE International Conference on Web Services (ICWS) |
Keywords | Field | DocType |
Handwriting Chinese Character Recognition,Convolutional Neural Network,Web Service,Online,Offline | Data mining,Chinese characters,Character recognition,Handwriting,Convolutional neural network,Computer science,Basic writing,Natural language processing,Artificial intelligence,Web service,Vocabulary | Conference |
ISBN | Citations | PageRank |
978-1-5386-7248-8 | 0 | 0.34 |
References | Authors | |
18 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hang Zhuang | 1 | 26 | 6.54 |
| Changlong Li | 2 | 26 | 6.88 |
| Xuehai Zhou | 3 | 551 | 77.54 |