Handwritten Character Recognition using Template Matching

TJPRC, 2013

Optical character recognition has become one of the most successful applications of technology in the field of pattern recognition and artificial intelligence. Optical Character Recognition deals with the problem of recognizing optically processed characters. Optical recognition is performed off-line after the writing or printing has been completed, as opposed to on-line recognition where the computer recognizes the characters as they are drawn. Optical Character Recognition by using Template Matching is a system prototype that useful to recognize the character or alphabet by comparing two images of the alphabet. The objectives of this system prototype are to develop a prototype for the Optical Character Recognition (OCR) system and to implement the Template Matching algorithm in developing the system prototype. This system prototype has its own scopes which are using Template Matching as the algorithm that applied to recognize the characters, characters to be tested are alphabet (A – Z), grey-scale images were used with Times New Roman font type and recognizing the alphabet by comparing between two images. The purpose of this system prototype is to solve the problem in recognizing the character which is before that it is difficult to recognize the character without using any techniques and Template Matching is as one of the solution to overcome the problem. The processes are starting from the acquisition process, filtering process, threshold the image, clustering the image of alphabet and lastly recognize the alphabet. All of these processes are very important to get the result of recognition after comparing the two character images.

Memory & Cognition, 1996

Psychological data suggest that internal representations such as mental images can be used as templates in visual pattern recognition. But computational studies suggest that traditional template matching is insufficient for high-accuracy recognition of real-life patterns such as handwritten characters. Here we explore a model for visual pattern recognition that combines a template-matching and a feature-analysis approach: Character classification is based on weighted evidence from a number of analyzers (demons), each of which computes the degree of match between the input character and a stored template (a copy of a previously presented character). The template-matching pandemonium was trained to recognize totally unconstrained handwritten digits. With a mean of 37 templates per type of digit, the system has attained a recognition rate of 95.3%, which falls short of human performance by only 2%-3%.

2005

This paper presents a survey of elastic matching (EM) techniques employed in handwritten character recognition. EM is often called deformable template, flexible matching, or nonlinear template matching, and defined as the optimization problem of two-dimensional warping (2DW) which specifies the pixel-to-pixel correspondence between two subjected character image patterns. The pattern distance evaluated under optimized 2DW is invariant to a certain range of geometric deformations. Thus, by using the EM distance as a discriminant function, recognition systems robust to the deformations of handwritten characters can be realized. In this paper, EM techniques are classified according to the type of 2DW and the properties of each class are outlined. Several topics around EM, such as the category-dependent deformation tendency of handwritten characters, are also discussed.

2003

A major feature of new mobiles terminals using penbased interfaces, such as personal assistants or e-book, is their personal character, implying that a good interface should be easily customizable in order to meet various users' needs. We proposed recently a new recognition engine with strong adaptation abilities that allows learning a user's writing style or new symbols easily. It dealt with characters with simple shape only; we describe here an extension of this recognition system that deals with more complex graphical symbols. We propose an adaptive learning scheme that can learn more and more as the writer gives new samples.

In this work, we describe a character recognition system we have implemented for experimenting with self-supervised adaptation method. The Dynamic Time Warping algorithm is used for matching input characters to prototypes and recognition is carried out according to the k-nearest neighbor rule. The prototype set is adapted by adding new prototypes into the prototype set and reshaping existing ones with a method based on the Learning Vector Quantization. The adaptation process is supervised by the user's reactions to the recognition results and other indirect information obtained from the user interface of text input. We also discuss the practical problems encountered in the implementation of a computationally heavy recognition method into a device with limited resources.