We propose a system that reads the text encountered in natural scenes with the aim to provide assistance to the visually impaired persons. This paper describes the sys- tem design and evaluates several character extraction meth- ods. Automatic text recognition from natural images re- ceives a growing attention because of potential applications in image retrieval, robotics and intelligent transport system. Camera-based document analysis becomes a real possibil- ity with the increasing resolution and availability of digital cameras. However, in the case of a blind person, finding the text region is the first important problem that must be addressed, because it cannot be assumed that the acquired image contains only characters. At first, our system tries to find in the image areas with small characters. Then it zooms into the found areas to retake higher resolution images nec- essary for character recognition. In the present paper, we propose four character-extraction methods based on con- nected components. We tested the effectiveness of our meth- ods on the ICDAR 2003 Robust Reading Competition data. The performance of the different methods depends on char- acter size. In the data, bigger characters are more prevalent and the most effective extraction method proves to be the se- quence: Sobel edge detection, Otsu binarization, connected component extraction and rule-based connected component filtering.
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