Self-circulation Intelligent Text Recognition Based on Multi-stage Data Generation
MA Xinqiang1,2,3, LIU Lina2, LI Xuewei4, GU Ye4, HUANG Yi1,2,3, LIU Yong2
1. College of Computer Science and Technology, Guizhou University, Guiyang 550025;
2. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027;
3. Institute of Intelligent Computing and Visualization Based on Big Data, Chongqing University of Arts and Sciences, Chong-qing 402160;
4. Material Branch, State Grid Zhejiang Electric Power Co.Ltd., Hangzhou 310000algorithm gains good recognition performance in multiple public English datasets and Chinese-specific complex text scenarios.
There are few effective big data annotation methods for both English and Chinese recognition in complex and diverse scenarios. Therefore, multi-stage data generation self-circulation training algorithm(MSDG-OCR) for complex and diverse text recognition scenarios is proposed. Text data is generated randomly according to the defined generated data parameters, and the data annotation process is omitted. Grounded on convolutional recurrent neural network(CRNN) model, multi-stage self-circulation training is carried out, and the recognition accuracy of the samples is continuously improved by controlling the data generation strategy during the loop process. Experiments show that the proposed.
马新强, 刘丽娜, 李雪维, 顾晔, 黄羿, 刘勇. 基于多阶段数据生成的自循环文本智能识别[J]. 模式识别与人工智能, 2020, 33(5): 468-476.
MA Xinqiang, LIU Lina, LI Xuewei, GU Ye, HUANG Yi, LIU Yong. Self-circulation Intelligent Text Recognition Based on Multi-stage Data Generation. , 2020, 33(5): 468-476.
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2020, Vol. 33 
