A system for automatic recognition and description of graphical primitives and their interconnections in a mixed text/graphics image is described. The input to the system is a high-resolution binary image obtained by scanning paper-based documents. The Hough transform is applied to the connected components of the image to locate and separate text strings of various font size and orientation. The graphics in the segmented image is processed to represent thin entities by their core lines and thick objects by their boundaries. Lines of various types are identified and segmented into straight line and curved line segments during this process. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Various hatching and filling patterns in the image are identified and described. The algorithm has been implemented as software, and its robustness and performance have evaluated tested using a number of test images. The results for a simple test image are reported.>

An algorithm is designed to automatically segment a discrete curve into appropriate number of parts, each of which is to be effectively represented by a circular arc. Thus a concise representation can be obtained. The algorithm suggested is computationally efficient. Extensive experiments were conducted in applying this technique to curves of various shapes. Results show that the representations generated are accurate for even very complex curves.>