Recognition of Printed Arabic Words with the Fuzzy ARTMAP Neural Network

Author(s): Amin, A. | Murshed, N. |

Year: 1999

Citation: Proceedings of the IJCNN 99 (International Joint Conference on Neural Networks)

Abstract: This paper presents a new method for the recognition of Arabic text using global features and fuzzy ARTMAP neural network. The method is divided into three major steps. The first step is digitization and pre-processing to create connected component. The second step is concerned with feature extraction, where global features of the input word are used to extract features such as number of subwords, number of peaks within the subword, number and position of the complementary character, etc., to avoid the difficulty of segmentation stage. The third step is the classification and is composed of a single fuzzy ARTMAP. The method was evaluated with 3255 images of 217 Arabic words with different fonts (each word has 15 samples), and the mean correct classification rate was 95.25%

Topics: Image Analysis, Applications: Character Recognition, Models: Fuzzy ARTMAP,

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