DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification
文献类型：会议论文
| 作者 | Linjie Xing; Yu Qiao |
| 出版日期 | 2016 |
| 会议名称 | ICFHR 2016 |
| 会议地点 | 深圳 |
| 英文摘要 | Text-independent writer identification is challenging due to the huge variation of written contents and the ambiguous written styles of different writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep powerful representation for recognizing writers. DeepWriter takes local handwritten patches as input and is trained with softmax classification loss. The main contributions are: 1) we design and optimize multi-stream structure for writer identification task; 2) we introduce data augmentation learning to enhance the performance of DeepWriter; 3) we introduce a patch scanning strategy to handle text image with different lengths. In addition, we find that different languages such as English and Chinese may share common features for writer identification, and joint training can yield better performance. Experimental results on IAM and HWDB datasets show that our models achieve high identification accuracy: 99:01% on 301 writers and 97:03% on 657 writers with one English sentence input, 93:85% on 300 writers with one Chinese character input, which outperform previous methods with a large margin. Moreover, our models obtain accuracy of 98:01% on 301 writers with only 4 English alphabets as input. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10017]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Linjie Xing,Yu Qiao. DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification[C]. 见:ICFHR 2016. 深圳. |
入库方式： OAI收割
来源：深圳先进技术研究院
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