Method for splitting and identifying character strings at complex interference

Abstract

The invention discloses a method for splitting and identifying character strings at complex interferences, which is characterized by comprising a learning phase and an identifying phase. The learning phase comprises the following steps of: splitting an image containing m characters into m pictures to form a multi-example learning packet, taking the same characters as a category, and classifying the packet and inputting to a base; and calculating an integrogram of the packet, extracting haar-like characteristics of the packet as an example of the packet, finding out key examples of each category by using a diversity density algorithm, and finally learning the key examples by using classified performances of an SVM (Support Vector Machine). The identifying phase comprises the following steps of: predicting the type of a new packet by using a learning result to identify the character strings. By using the method, the function of automatically identifying the character strings at the complex interferences can be realized, and the identifying speed and efficiency are higher.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a segmentation and recognition technology of character strings under complex interference. Background technique [0002] After years of development, Optical Character Recognition (OCR) technology has made great progress and has been widely used in fields such as handwriting input, automatic license plate recognition, and automatic text scanning and recognition. However, the existing OCR technology is still difficult to robustly segment and recognize character strings under complex interference. Because of this, a character string subject to certain disturbances is usually used as a verification code on the network to identify whether an operation is a manual behavior or an automatic computer behavior. [0003] At present, the recognition methods for character strings are mainly divided into two categories, one is based on Euclidean space distance methods, such as template matchin...

AI Technical Summary

Problems solved by technology

[0006] (2) Characters stick together and are not easy to distinguish;

[0007] (3) There is no obvious difference between the interference and the characteristics of the character itself

This type of method has the ability of machine learning, can automatically count the characteristics of samples, and has a high recognition rate and fast recognition speed; Good learning effect, so the recognition rate of strings under complex interference is low

[0010] For strings that cannot be removed by algorithms, the above two methods require manual removal of interference and making a large number of samples

Doing so is time consuming and laborious

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