Associating text and graphics for scientific chart understanding

This paper presents our recent work that aims at associating the recognition results of textual and graphical information contained in the scientific chart images. Text components are first located in the input image and then recognized using OCR. On the other hand, the graphical objects are segmented and form high level symbols. Both logical and semantic correspondence between text and graphical symbols are identified. The association of text and graphics allows us to capture the semantic meaning carried by scientific chart images in a more complete way. The result of scientific chart image understanding is presented using XML documents.