Recognition results classification and post-processing methods for painted characters on billet surface
Recognition results classification and post-processing methods for painted characters on billet surface作者机构：School of Mechatronic Engineering and AutomationShanghai Key Laboratory of Intelligent Manufacturing andRobotics Shanghai University Shanghai 200072 People's Republic of China
出 版 物：《Advances in Manufacturing》 (先进制造进展（英文版）)
年 卷 期：2017年第5卷第3期
页 面：261-270页
核心收录：
学科分类：08[工学]
主 题：Painted character· Character segmentation.Character recognition · Recognition results classification·Post processing method
摘 要：Automatic identification of characters marked on billets is very important for steelworks to achieve manu- facturing and logistics informatization management. Due to the presence of adhesions, fractures, blurs, and other problems in characters painted on billets, character recognition accuracy with machine vision is relatively low, and hardly meets practical application requirements. To make the character recognition results more reliable and accu- rate, an identification results classification and post-pro- cessing method has been proposed in this paper. By analyzing issues in the image segmentation and recognition stage, the recognition result classification model, based on character encoding rules and recognition confidence, is built, and the character recognition results can be classified as correct, suspect, or wrong. In the post-processing stage, a human-machine-cooperation mechanism with a post- processing interface is designed to eliminate error infor- mation in suspect and wrong types. The system was developed and experiments conducted with images acquired in an iron and steel factory. The results show the character recognition accuracy to be approximately 89% using the character recognizer. However, this result cannot be directly applied in information management systems. With the proposed post-processing method, a human worker will query the suspect and wrong results classified by the system, determine whether the result is correct or wrong, and then, correct the wrong result through the post-processing interface. Using this method, the character recognition accuracy ultimately improves to 99.4%. Thus, the results will be more reliable applied in a practical system.
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