Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You and Your Team.

Learn More →

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... Automatic identification of characters marked on billets is very important for steelworks to achieve manufacturing 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 accurate, an identification results classification and post-processing 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 information 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Manufacturing Springer Journals

Recognition results classification and post-processing methods for painted characters on billet surface

Loading next page...
 
/lp/springer-journals/recognition-results-classification-and-post-processing-methods-for-G6VWF0JjsX
Publisher
Springer Journals
Copyright
Copyright © 2017 by Shanghai University and Springer-Verlag GmbH Germany
Subject
Engineering; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics; Nanotechnology and Microengineering
ISSN
2095-3127
eISSN
2195-3597
DOI
10.1007/s40436-017-0190-9
Publisher site
See Article on Publisher Site

Abstract

Automatic identification of characters marked on billets is very important for steelworks to achieve manufacturing 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 accurate, an identification results classification and post-processing 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 information 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.

Journal

Advances in ManufacturingSpringer Journals

Published: Aug 30, 2017

References