Handwritten Word Recognition Using MLP based Classifier: A Holistic Approach

Holistic Word Recognition is one of the new modalities for handwritten word identification. The holistic paradigm in handwritten word recognition treats the word as a single, indivisible entity and attempts to recognize words from their overall shape, as opposed to recognize the individual characters comprising the word. In the present work reports a longest-run based holistic feature, that has been used to classify word images belonging to different classes, using a neural network based classifier. To evaluate the technique, a few words from the handwritten documents of the CMATERdb1.2.1 dataset have been used. Frequently occurring English words are manually extracted from the handwritten pages and the accuracy of the technique is evaluated using a three fold cross-validation method. The best-case and average-case performances of the technique to the said data set are 89.9% and 83.24% respectively.