A method for text localization and recognition in real-world images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Color image segmentation using tensor voting based color clustering
Pattern Recognition Letters
Text extraction from natural scene image: A survey
Neurocomputing
Integrating multiple character proposals for robust scene text extraction
Image and Vision Computing
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Robust extraction of text from scene images is essential for successful scene text recognition. Scene images usually have non-uniform illumination, complex background, and existence of text-like objects. The common assumption of a homogeneous text region on a nearly uniform background cannot be maintained in real applications. We proposed a text extraction method that utilizes user's hint on the location of the text within the image. A resizable square rim in the viewfinder of the mobile camera, referred to here as a 'focus', is the interface used to help the user indicate the target text. With the hint from the focus, the color of the target text is easily estimated by clustering colors only within the focused section. Image binarization with the estimated color is performed to extract connected components. After obtaining the text region within the focused section, the text region is expanded iteratively by searching neighboring regions with the updated text color. Such an iterative method would prevent the problem of one text region being separated into more than one component due to non-uniform illumination and reflection. A text verification process is conducted on the extracted components to determine the true text region. It is demonstrated that the proposed method achieved high accuracy of text extraction for moderately difficult examples from the ICDAR 2003 database.