Systems. When machine generated text is prdnted against clean backgrounds, it can be converted to a computer readable form (ASCII) using current Optical Character Recognition (OCR) technology. However, text is often printed against shaded or textured backgrounds or is embedded in images. Examples include maps, advertisements, photographs, videos and stock certificates. Current document segmentation and recognition technologies cannot handle these situafons well. In this paper, a four-step system which automaticnlly detects and extracts text in images i& proposed. First, a texture segmentation scheme is used to focus attention on regions where text may occur. Second, strokes are extracted from the segmented text regions. Using reasonable heuristics on text strings such as height similarity, spacing and alignment, the extracted strokes are then processed to form rectangular boxes surrounding the corresponding ttzt strings. To detect text over a wide range of font sizes, the above steps are first applied to a pyramid of images generated from the input image, and then the boxes formed at each resolution level of the pyramid are fused at the image in the original resolution level. Third, text is extracted by cleaning up the background and binarizing the detected ted strings. Finally, better text bounding boxes are generated by srsiny the binarized text as strokes. Text is then cleaned and binarized from these new boxes, and can then be passed through a commercial OCR engine for recognition if the text is of an OCR-recognizable font. The system is stable, robust, and works well on imayes (with or without structured layouts) from a wide van'ety of sources, including digitized video frames, photographs,
Wu et al. (Wed,) studied this question.