Text detection in natural images remains a very challenging task. For instance, in an urban context, the detection is very difficult due to large variations in terms of shape, size, color, orientation, and the image may be blurred or have irregular illumination, etc. In this paper, we describe a robust and accurate multiresolution approach to detect and classify text regions in such scenarios. Based on generation/validation paradigm, we first segment images to detect character regions with a multiresolution algorithm able to manage large character size variations. The segmented regions are then filtered out using shapebased classification, and neighboring characters are merged to generate text hypotheses. A validation step computes a region signature based on texture analysis to reject false positives. We evaluate our algorithm in two challenging databases, achieving very good results.
We offer, in this paper, a new method to segment text in natural scenes. This method is based on the use of a morphological operator: the Toggle Mapping. The efficiency of the method is illustrated and the method is compared, according to various criteria, with common methods issued from the state of the art. This comparison shows that our method gives better results and is faster than the state of the art methods. Our method reduces also the number of segmented regions. This can lead to time saving in a complete scheme (executing time of multiple processing steps usually depends on the number of regions) and proves that our algorithm is more relevant.
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