Vision based handwritten character recognition

Abstract

Online automatic recognition of handwritten text has been an ongoing research problem for four decades. It is used in automated postal address and ZIP code and form reading, data acquisition in bank checks, processing of archived institutional records, automatic validation of passports, etc. It has been gaining more interest lately due to the increasing popularity of handheld computers, digital notebooks and advanced cellular phones. Traditionally, human-machine communication has been based on keyboard and pointing devices. Online handwriting recognition promises to provide a dynamic means of communication with computers through a pen like stylus, not just an ordinary keyboard. This seems to be a more natural way of entering data into computers. In this thesis, we develop a character recognition system that combines the advantage of both on-line and off-line systems. Using an USB CCD Camera, positions of the pen-tip between frames are detected as they are written on a sheet of regular paper. Then, these positions are used for calculation of directional information. Finally, handwritten character is characterized by a sequence of writing directions between consecutive frames. The directional information of the pen movement points is used for character pre-classification and positional information is used for fine classification. After characters are recognized they are passed to LaTeX code generation subroutine. Supported LaTeX environments are array construction, citation, section, itemization, equation, verbatim and normal text environments. During experiments a recognition rate of 90% was achieved. The main recognition errors were due to the abnormal writing and ambiguity among similar shaped characters.