License plate recognition with hierarchical temporal memory model

Abstract

Development of high quality license plate recognition system is a challenging task, not fully solved nowadays. License plate recognition process consists of the following steps: license plate detection, individual characters segmentation and recognition. This paper contains methods, connected with license plate allocation, segmentation and characters recognition. The noise on the plate and its angular inclination are main problems raised during developing such systems. In this article a new method of license plate recognition is presented. The proposed method includes preliminary image filtering, connected component method for segmentation and hierarchical temporal memory model for recognition. Image prefiltering improves the efficiency of subsequent binarization. Generally, license plate segmentation is provided by the histogram method, with different angles of inclination of the registration plate. As a result the rotation of the plate reduces the image quality. The connected component method eliminates rotation from this process, and provides no loss of image quality. Separate symbols can be represented under a small angle after such segmentation, which could complicate their identification. However, the application of the hierarchical temporal memory model for character recognition, previously trained at the sloping characters images, gives positive results. The proposed algorithms can also be used for distorted text segmentation and recognition.