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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Weixin Yang Lianwen Jin Manfei Liu |
| Copyright Year | 2015 |
| Description | Author affiliation: Coll. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China (Weixin Yang; Lianwen Jin; Manfei Liu) |
| Abstract | Most existing online writer-identification systems require that the text content is supplied in advance and rely on separately designed features and classifiers. The identifications are based on lines of text, entire paragraphs, or entire documents; however, these materials are not always available. In this paper, we introduce a path-signature feature to an end-to-end text-independent writer-identification system with a deep convolutional neural network (DCNN). Because deep models require a considerable amount of data to achieve good performance, we propose a data-augmentation method named DropStroke to enrich personal handwriting. Experiments were conducted on online handwritten Chinese characters from the CASIA-OLHWDB1.0 dataset, which consists of 3,866 classes from 420 writers. For each writer, we only used 200 samples for training and the remaining 3,666 for testing. The results reveal that the path-signature feature is useful for writer identification, and the proposed DropStroke technique enhances the generalization and significantly improves performance. |
| Starting Page | 546 |
| Ending Page | 550 |
| File Size | 1217201 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479918058 |
| DOI | 10.1109/ICDAR.2015.7333821 |
| 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 | Pipelines Accuracy Handwriting recognition Image recognition Chinese character Deep convolutional neural network online textindependent writer identification data augmentation pathsignature feature |
| Content Type | Text |
| Resource Type | Article |
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