Effective Handwriting Recognition System Using Geometrical Character Analysis Algorithms Wojciech Kacalak and Maciej Majewski Koszalin University of Technology, Department of Mechanical Engineering Raclawicka 15-17, Koszalin, Poland {wojciech.kacalak,maciej.majewski}@tu.koszalin.pl Abstract. We propose a new method for natural writing recognition that utilizes geometric features of letters. The paper deals with recogni- tion of isolated handwritten characters using an artificial neural network. As a result of the geometrical analysis realized, graphical representations of recognized characters are obtained in the form of pattern descriptions of isolated characters. The radius measurements of the characters ob- tained are inputs to the neural network for natural writing recognition which is font independent. In this paper, we present a new method for off-line natural writing recognition and also describe our research and tests performed on the neural network. Keywords: handwriting recognition, artificial neural networks, artificial intelligence, human-computer interaction, natural writing processing. 1 Introduction Natural writing recognition has been studied for nearly forty years and there have been many proposed approaches. The problem is quite complex, and even now there is no single approach that solves it both efficiently and completely in all contexts. In written language recognition processes, an image containing text must be appropriately supplied and preprocessed. Then the text must either undergo segmentation or feature extraction. Small processed pieces of the text will be the result, and these must undergo recognition by the system. Finally, contextual information should be applied to the recognized symbols to verify the result. Artificial neural networks, applied in handwriting recognition, allow for high generalization ability and do not require deep background knowledge and formalization to be able to solve the written language recognition problem. Handwriting recognition can be divided by its input method into two cate- gories: off-line handwriting recognition and on-line handwriting recognition. For off-line recognition, the writing is usually captured optically by a scanner. For on-line recognition, a digitizer samples the handwriting to time-sequenced pixels as it is being written. Hence, the on-line handwriting signal contains additional time information which is not present in the off-line signal. T. Huang et al. (Eds.): ICONIP 2012, Part IV, LNCS 7666, pp. 248–255, 2012. c  Springer-Verlag Berlin Heidelberg 2012