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Scene text information extraction plays an important role in many computer vision applications. Most features in existing text extraction algorithms are only applicable to one text extraction stage (text detection or recognition), which significantly weakens the consistency in an end-to-end system, especially for the complex Chinese texts. To tackle this challenging problem, we propose a novel text...
Text is a vital feature in applications of computer vision. Traditional Chinese character recognition techniques are mainly based on optical character recognition (OCR), however, they can't obtain satisfactory results from images affected by complex circumstance, such as different viewpoint, scale changes, addition of noise and complex background. To solve these problems, inspired by SIFT descriptor,...
The reference presents a novel approach for Chinese character recognition. Based on it, we know that we can treat character recognition as an image matching problem. Compared with traditional OCR, the new approach for character recognition uniquely uses local invariant descriptors as a new feature extraction method. In this paper, we present a new local descriptor which combines the scale-invariant...
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