The object of this paper is to describe an approach to decompose and describe a complex text/graphic into overlapping (either polygonal or curved) meaningful shapes, and reconstruct the original image from the description file. To narrow and optimize the search, some heuristic decision making functions are implemented into the system. In this paper algorithms are designed to automate the process of generation of loops with minimum redundancy from the bit map of the image and identify those loops thus generated if they are simple ones. For the complex loops, decompose them into simpler interpretable shapes or curved segments. Finally, a succinct description file is established for the whole image. Effectiveness of the algorithm has been evaluated through experiments on a large number of graphics taken on overlapped objects. Results show that the algorithm developed is computationally efficient. Once the image is decomposed and the meaningful component parts (either polygonal or curved objects if regular in shape) are identified, the data reduction achieved through this succinct description is extremely high, thus alleviating the matching process. Even for those silhouettes of curved shape, an approach, called concatenated-arc representation, is developed for their description. With this concatenated arc approach, much fewer number of arc segments are needed than needed by line segment approximation. Shapes reconstructed from these description files match closely with the original ones, even for the very complex curves.