Gradient image and similarity weight based digit identification method

Abstract

The invention discloses a gradient image and similarity weight based digit identification method. The method comprises the following steps: inputting an image needing to be identified; according to prior knowledge, determining a candidate area; performing image enhancement and segmentation on the candidate area; pre-determining whether digits exist in the image; performing area marshalling; carrying out template coupling on an original image on the basis of a gradient image and a similarity weight; and updating a template database. According to the invention, the candidate area is determined according to the prior knowledge so that the algorithm efficiency is improved and the misjudgment possibility is reduced. Through pre-determining whether the digits exist in the image, the average identification efficiency by use of the method can be improved by about 10%. Through respectively performing weighting on two correlation coefficients, a final correlation coefficient is obtained and determination identification is carried out, so that the average identification correct rate can be improved by one to two percentage points.

Description

technical field [0001] The invention relates to a digital recognition method. Background technique [0002] Machine vision is characterized by automation, objectivity, non-contact and high precision. Compared with image processing systems in the general sense, machine vision systems emphasize accuracy, speed, and reliability in industrial field environments. Because the machine vision system can quickly acquire a large amount of information, it is easy to process automatically, and it is also easy to integrate with design information and processing control information. Therefore, in the modern automated production process, the machine vision system is widely used in condition monitoring, finished product inspection and quality control. field. The machine vision system can improve the flexibility and automation of production. In some dangerous working environments that are not suitable for manual work or where artificial vision is difficult to meet the requirements, machine...

AI Technical Summary

Problems solved by technology

The place where the camera is set up must keep an appropriate distance from the monitored object and at the same time ensure that the camera can work normally under high temperature conditions. However, due to the occlusion of objective objects in the steelmaking plant, some cameras may not be able to obtain detailed images of the monitored object, so they cannot be identified. Targets, artificial occlusions are not very common in the imaging process. If the worker happens to be on the same line with the camera and the subject during the operation, it may hinder the imaging of the subject. However, since most of the targets are moving Therefore, this kind of occlusion generally does not affect the final target recognition result

[0007] In short, the complex imaging environment on site makes the traditional method of using shape recognition numbers unable to achieve satisfactory results

Images