Chip Refinement Character Recognition Text Clean - up I 2 Segmentation Texture Segmentation Texture Segmentation Texture Segmentation Texture Generation

There are many applications in which the automatic detection and recognition of text embedded in images is useful. These applications include multimedia systems, digital libraries, and Geographical Information Systems. When machine generated text is printed against clean backgrounds, it can be converted to a computer readble 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 certiicates. Current OCR and other document segmentation and recognition technologies cannot handle these situations well. In this paper, a system that automatically detects and extracts text in images is proposed. This system consists of four phases. First, by treating text as a distinctive texture, a texture segmentation scheme is used to focus attention on regions where it 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 tight rectangular bounding boxes around the corresponding text strings. To detect text over a wide range of font sizes, the above steps are rst applied to a pyramid of images generated from the input image, and then the boxes formed at each resolution of the pyramid are fused at the original resolution. Third, an algorithm which cleans up the background and binarizes the detected text is applied to extract the text from the regions enclosed by the bounding boxes in the input image. Finally, text bounding boxes are reened (regenerated) by using the extracted items as strokes. These new boxes usually bound text strings better. The clean-up and binarization process is then carried out on the regions in the input image bounded by the boxes to extract cleaner text. The extracted text can then be passed through a commercial OCR engine for recognition if the text is of an OCR-recognizable font. Experimental results show that the algorithms work well on images from a wide variety of sources, including newspapers, magazines, printed advertisements, photographs, digitized video frames, and checks. The system is also stable and robust|the system parameters work for all the experiments.