Design and Research of Industrial High Availability Character Recognition Technology

With the development of deep learning and neural networks, OCR technology has made great progress in character recognition and text analysis, and has gradually penetrated into various industrial fields with the landing application of artificial intelligence. However, in practical application, how to improve the accuracy and robustness of recognition, and how to identify complex layout documents from complex background images are still the technical difficulties of OCR. This paper studies multi-line and multi-segment discontinuous text recognition in industrial applications, proposes a CRNN character recognition model based on the soft attention mechanism of weighted attention weights, and combines electronic fence calibration technology, non-local mean denoising technology and other pre-processing technologies. And the post-processing methods such as sliding window suppression and pre-defined configuration ratio can improve the detection rate and recall rate. The model is improved on the basis of LSTM in the cycle layer. The improved model and related pretreatment methods are embedded in the EasyOCR framework, and tested in the traceability system of an intelligent cement prefabricated board factory. The experiment shows that the scheme can deal with scenarios with serious pollution and complex background, and has high reliability and high availability. Number identification and traceability system suitable for various production lines.