A segmentation based adaptive approach for cursive handwritten text recognition
The paper presents a segmentation based adaptive approach for the learning and recognition of single person’s handwritten text. The approach is incorporated into an automated intelligent system for scanning of handwritten text on a paper and converting it into a text file. It scans an A4 size handwritten page and segments it into lines, words and characters. The segmented characters are passed to a neural classifier for the recognition. The final word is passed through a lexicon based matching process to improve the accuracy of the recognized text. Two neural networks are investigated for the learning of segmented characters quickly and accurately. The experimental results show that the proposed approach can produce high text recognition accuracy with a small number of training samples.