Algorithm for Fast Detection and Identification of Characters in Gray-level Images

This paper discusses methods for character extraction based on statistical and structural features of gray levels, and proposes a dynamic local contrast accommodating line width. Precision locating of character groups is realized by exploiting horizontal projection and character arrangements of binary images along horizontal and vertical directions respectively. Also discussed is the method for segmentation of characters in binary images, which is based on projection taking into account stroke width and character sizes. A new method for character identification is explored, which is based on compound neural networks. A complex neural network consists of two sub-nets, with the first sub-net performing self-induction of patterns via 2-dimentional local-connected 3order networks, the second sub-set linking up a locally connected BP networks performing classification. Reinforced reliability of the network recognition by introducing conditions for identification denial. Experiments confirm that the proposed methods possess impressive robustness, rapid processing and high accuracy of identification.