Performance improvement of character recognition in industrial applications using prior knowledge for more reliable segmentation |
| |
Authors: | Martin Grafmüller Jürgen Beyerer |
| |
Affiliation: | 1. Karlsruhe Institute of Technology (KIT), Vision and Fusion Laboratory, Adenauerring 4, 76131 Karlsruhe, Germany;2. Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), 76131 Karlsruhe, Germany |
| |
Abstract: | In industrial applications optical character recognition with smart cameras becomes more and more popular. Since these applications mostly have challenging environments for the systems it is most important to have very reliable character segmentation and classification algorithms. The investigations of several algorithms have shown that character segmentation is one if not the main bottleneck of character recognition. Furthermore, the requirements of robust and fast algorithms related to skew angle estimation and line segmentation, as well as tilt angle estimation, and character segmentation are high. This is the reason for introducing such algorithms that are specifically adapted to industrial applications. Additionally, a method is proposed that is based on the Bayes theorem to take account of prior knowledge for line and character segmentation. The main focus of the investigations of the character recognition system is recognition performance and speed, since real-time constraints are very hard in industrial application. Both requirements are evaluated on an image series captured with a smart camera in an industrial application. |
| |
Keywords: | Character segmentation Prior knowledge Character recognition |
本文献已被 ScienceDirect 等数据库收录! |
|