Handwritten character recognition using low resolutions

Abstract

This paper deals with an experimental study into the recognition of handwritten characters using representations of low resolution. For this experiment several methodologies were used, such as an attributed graph OCR method, a back-propagation neural network (BNN), a recurrent neural network (RNN), and the Castor neural network (CNN). The attributed graph methodology is based on the representation (mapping) of the text characters onto a small size two-dimensional array of 12×9 cells. For the recognition process each character is considered as a composition of “main” and “secondary” features. The main features are the important parts of a character for its successful recognition. The secondary (or artistic) features are the parts of a character that contribute to its various representations. The attribute graph methodology presented in this paper attempts to prove that the recognition of a reduced-size character provides a robust approach for the recognition of handwritten text. The BNN approach is used here as a comparative recognition method, although it has some serious weaknesses at low resolution. The RNN approach for handwritten character recognition is based upon recurrent neural networks, which have a feedback mechanism. The feedback mechanism acts to integrate new values of feature vectors with their predecessors. The output is supervised according to a target function. These networks can deal with inputs and outputs that are explicit functions of time. A new way of associating shape information was used, which gives very consistent results for handwritten character recognition. In this scheme the “shadow” of each character was considered, to find the distances between the margins of the character. The distances are normalized with respect to the maximum distance in the entire shape to minimize the effect of disproportionally formed characters. In addition, the performance of Castor’s neural net was evaluated for the recognition of handwritten text characters, by using character data sets with various resolutions. For this effort the three neural networks and attributed graph approaches used a set of 5000 handwritten characters, and their results are compared.