Error Detection and Correction in Toponym Recognition in Cartographic Maps

At present a lot of methods and programs for automatic text recognition exist. However there are no effective text recognition systems for graphic documents. Graphic documents usually contain a great variety of textual information. As a rule the text appears in arbitrary spatial positions, in different fonts, sizes and colors. The text can touch and overlap graphic symbols. The text meaning is semantically much more ambiguous in comparison with standard text. To recognize a text of graphical documents, it is necessary first to separate it from linear objects, solids, and symbols and to define its orientation. Even so, the recognition programs nearly always produce errors. In the context of raster-to-vector conversion of graphical documents, the problem of text recognition is of special interest, because textual information can be used for verification of vectorization results (post-processing). In this work, we propose a method that combines OCRbased text recognition in raster-scanned maps with heuristics specially adapted for cartographic data to resolve the recognition ambiguities using, among other information sources, the spatial object relationships. Our goal is to form in the vector thematic layers geographically meaningful words correctly attached to the cartographic objects.