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Document Image Classification Using Probabilistic Neural Network | ||
| Al-Ma'mon College Journal | ||
| Article 1, Volume 0, Issue 15, 2018, Pages 63-77 | ||
| Authors | ||
| Wdad A. Nasir; Aml A. Kadhim | ||
| Abstract | ||
| The decreasing cost of hardware will eventually enable commonplace the storage and process of documents by electronic means. However, today most documents are begin saved, processed, and presented on papers. Paper is the primary medium for books, journals and newspapers. In order to properly archive, index and process a large number of document images, several challenging processing steps must be completed. In this work, a proposed system is used to segment and classify the gray document image to regions based on data blocks. A probabilistic neural network model has been used for this purpose . First, the preprocessing is used to convert gray document image to binary document image, then the erode process used to convert the binary document into blocks. The resulting blocks are segmented to number of regions by using label procedure, the four features of each region are calculated based on the bounding box for each region. Then these features are fed to the input layer of a probabilistic neural network for classification to one of two regions (text, picture). Some gray documents images are used in order to test the proposed system. The tests have successfully classified all test documents. | ||
| Keywords | ||
| Probabilistic Neural Network | ||
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