Performance improvement of character recognition in industrial applications using prior knowledge for more reliable segmentation

• The algorithms are developed to be real-time capable and robust to noise or illumination changes.
• We show how to improve classification accuracy and speed using prior knowledge, e.g., number of lines or characters, in character segmentation.
• Prior knowledge in segmentation also improves the reliability of the segmentation algorithm.
• Comparison of different combinations of features and classifiers to show differences in accuracy and processing time.

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.