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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bolan Su Xi Zhang Shijian Lu Chew Lim Tan |
| Copyright Year | 2015 |
| Description | Author affiliation: Inst. for Infocomm Res., A*Star, Singapore, Singapore (Bolan Su; Shijian Lu) || Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore (Xi Zhang; Chew Lim Tan) |
| Abstract | Recognition of handwritten text is a useful technique that can be applied in different applications, such as signature recognition, bank check recognition, etc. However, the off-line handwritten text recognition in an unconstrained situation is still a very challenging task due to the high complexity of text strokes and image background. This paper presents a novel segmented handwritten text recognition technique that ensembles recurrent neural network (RNN) classifiers. Two RNN models are first trained that take advantage of the widely used geometrical feature and the Histogram of Oriented Gradient (HOG) feature, respectively. Given a handwritten word image, the optimal recognition result is then obtained by integrating the two trained RNN models together with a lexicon. Experiments on public datasets show the superior performance of our proposed technique. |
| Starting Page | 386 |
| Ending Page | 390 |
| File Size | 964638 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479918058 |
| DOI | 10.1109/ICDAR.2015.7333789 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-23 |
| Publisher Place | Tunisia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Computational modeling Handwriting recognition Artificial neural networks |
| Content Type | Text |
| Resource Type | Article |
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