Recogniton Technology Research On Tire Specifications Of High Noise Target Image

Tire Specifications is character printed in sidewall of the body by manufacturers on behalf of specific significance. These parameters is very important to identify the type of classification for tire factory management, used as a important parameters for production management,quality of tracing.At present, OCR technology is widely used in character recognition, but tire specifications have not yet identified its research and related reporting in the domestic. A more mature technology abroad for the DOT Code and Tire Specifications identification is the CCD imaging with scanning of line laser, this identification system using triangulation measurement technology, the advantage of identification of tire molding character are reliable and robust, the disadvantage are high cost,long processing time and needing line laser equipment,CCD imaging systems and mechanical structure, moreover, it does not recognize the printed characters.Based on the analysis of the history and recent developments around tire Specifications identification, study was carried out. The article analysed characteristics of the target image, a system was presented that included LED lighting arrays, CMOS imaging, as well as identification of BP neural network, which low-cost, fast and effective identification of different fonts of tire specifications with big background noise. Among them, in image pre-processing, the use of iterative least square method used to fit accurate positioning of the circle center , and proposed integral windowed statistics to extract specifications character region, a edge adaptive localization algorithm for segmentation of individual characters,which effectively ruled out big noises. In specifications recognition, research was developed by using a variety of characteristics extracted, and combined methods were used to identify tire specifications with classifier of BP neural network algorithm-based classifier and secondary classifier recognition algorithm of specifications, which have high recognition rate and the less time-consuming. The identification system meeted the rapid testing requirements and came up to the desired effect.The following are the main innovations.1. Lighting technology research for Tire image capture.As tire image background and character prospects is the same kinds of rubber material, the surface color difference is very small, therefore the image acquisition are with noise and low contrast, this paper adopts specific design structure of a ring array of LED lighting, effectively reducing the image noise, increase image contrast.2. Extraction algorithms for recognition object of high noise background. Algorithm using integral window to reduce the dimensions of the image edge map, from two-dimensional to one-dimensional, and to make use of one-dimensional signals to form the sawtooth extreme points, based on sawtooth waveform parameters and specifications of its rules to extract the number of characters in the region ; for the segmentation of individual characters in the complexity of the issue and put forward edge adaptive localization algorithm, the algorithm uses edge-connection strength,edge strength and character length and using threshold from small to large, regional division multiple iterations to locate the characters in descending . Algorithm for anti-noise capability, suitable for a complex environment, precise positioning of characters.3.Combining with the relation features of an integrated recognition algorithm of tire specifications. According to the characteristics of specification characters, in-depth study of the tire specification character recognition features, recognition algorithm of specifications was presented based on BP neural network character recognition; and secondary classifier recognition algorithm of specifications was proposed based on the relationship features . For the noise, confusion of character information proposed method to obtain the truncated characters are not fully distinguish between characters feature algorithm to improve character recognition rate. Experimental results show that the comprehensive specifications recognition algorithm combined with relationship features is adaptive of tire specifications recognition..