Character Degradation Model and HMM Word Recognition System for Text Extracted from Maps

Abstract

Geographic maps are one of the most abundant and valuable sources of accurate information about various features of bodies of land and water. Due to their importance in applications ranging from city planning and navigation to tracking changes in vegetation cover and coastline erosion, most countries have established dedicated organizations that are responsible for the production and maintenance of maps that cover their entire territories. This has resulted in the production and availability of a tremendous quantity of useful information about every part of the world. Aswith other types of documents, newmaps are nowadays produced in specialized computer programs, and are easy to manage and update since the individual information layers that form the whole map are available to the map producer. In addition to the change in the method of production, the applications and ways to process maps have also changed in the past few decades. Geographic Information Systems (GIS) have provided new means to analyze, process, visualize, and integrate various forms of geographic data. The input to these systems can be satellite or aerial imagery, remote sensing data (e.g. LIDAR images), raster or vector representations of geographic maps, and any other type of data that are related to locations (e.g. local populations, crime statistics, and textual information). Due to their wide availability, accuracy, and relative cheapness compared to other types of geo-referenced data, geographic maps are probably the most widely used source of information for GIS users. However, the majority of existing geographic maps exist only in printed form. This means that unlike the case for computer generated maps, printed maps cannot be directly used as the input to a GIS since both the end users and the map producers only have access to the dense and complex mixture of regularly intersecting and/or overlapping set of graphical and textual elements rather than the individual features of interest. Currently the only reliable way of converting printed maps into computer readable format is to have a highly trained operator manually extract the individual sets of features (graphical and textual). The manual feature extraction methods consist of digitization using a digitizing tablet, and heads-up digitizing. In the first method, the paper map is placed on top of the digitizing tablet, and the operator traces over lines and other objects of interest using a stylus or a digitizing puck (a device with crosshairs and multiple buttons that enable data entry operations). In heads-up digitizing (otherwise known as on-screen digitizing) on the other hand, the paper map is first scanned into a digital image. The operator then traces over every single object of interest on the computer screen using a mouse. Since zooming into difficult 4