Maps character recognition : the University of the Philippines map a case study - Vibar, Franklin N. (1995)
In the University of the Philippines, where the street layout is quite complicated, the buildings are far from each other, and where visitors are quite frequent; a tool is very essential in order to guide these visitors to find the right place at the right time. A map is good, but some people get annoyed with maps since looking for a small place in a very big map will also take some time. Not if it is automated (computerized), so that what the person needed to do is to input the place s/he is looking for and at one instance will be displayed to her/him. Now what we need to do is first to convert maps on paper into files, where the process is called scanning and the equipment a scanner, so that it can be processed and be displayed on the screen. Then, some processes will be followed so that the words in the maps will be automatically be properly recognized. A list will then be generated which will contain each of the words recognized and the corresponding position of it in the map, which in turn will be the position desired by the user whenever a particular place, in the list, is accessed. The basic concepts needed in making the project are scanning, image processing, database and most importantly character recognition. Scanning is for the conversion of the paper to digital (file) form; image processing is for the display of the images for the user; and database is for the management and access of the generated list. Basically the recognition processes followed are the processes followed by the Optical Character Recognition System (O.C.R.) namely characters on input document, optical scanner, location and segmentation of characters, preprocessing, feature extraction, recognition and decision, and postprocessing. The input document is the map and the characters are the name of the places. The optical scanner is the equipment used to convert a map on paper into a digital image in a file so that needed processing will be done by the computer. Location and segmentation means converting the digital image into a very large matrix where the entries corresponds to the color value of the pixel in the image. Preprocessing means that, noise are removed from the image and doing further enhancement to the image. Feature extraction means getting the characteristic of each of the character that would be very essential in its proper recognition. Recognition is the classification of a character as a particular character. And post-processing is the formation of the words where each of the characters belong. The recognition technique that we used is template matching, which is the most appropriate for the project since, one font type, one size, single orientation and exact matching for characters are used. This is considered as a limitation of the project, and as a recommendation, remove them for more flexibility. Another recommendation is to add more relevant features to the project, and not just the input and display option.