Method for determining connection sequence of cascade classifiers with different features and specific threshold

Abstract

The invention relates to a method for fixing the connecting sequence and character threshold value of cascade classifiers with different characters, wherein said cascade classifiers are used to extract selected connecting components from the candidate connecting components decomposed from image; said method comprises: first decomposing at least one image to obtain several connecting components as present array; then parallel feeding present sample into cascade classifier, to circulated train, to fix the connecting sequence and character threshold value of cascade classifiers. The invention also provides a method for obtaining selected image from one image, which uses the cascade classifiers via aforementioned method, to quickly remove non-selected connecting components, to spend more cost on selected connecting components, to improve the image processing speed and improve the obtaining accuracy.

Description

technical field [0001] The invention clearly relates to the field of digital image processing, and in particular to a method for determining the connection sequence and feature threshold of a set of cascade classifiers with different features, and using the cascade classifier group formed by the method to obtain selected The method of specifying the image. Background technique [0002] Text detection and segmentation in natural scenes has many applications. With the increase of high-performance, low-cost, and portable digital imaging equipment, the application of scene text recognition is expanding rapidly. By using a camera connected to a mobile phone, PDA or other dedicated digital devices, we can easily capture the text around us: such as road names, advertisements, traffic warnings, restaurant menus, and so on. Automatic recognition, translation and pronunciation of these texts can be of great help to overseas tourists, visually impaired p...

AI Technical Summary

Problems solved by technology

The disadvantage of this type of method is that all heuristic rules are in a fixed order, and their thresholds are manually input empirical values, which are usually unstable and cannot guarantee robust detection results

In addition, a strong classifier (such as Support Vector Machine, SupportVector Machine, SVM) can be used to extract text connected components from candidate connected components. The disadvantage of this type of method is that all features must be calculated for each connected component, and the amount of calculation and time consuming

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