Cursive Handwritten Word Recognition

1. Intelligent Segmentation

Artificial Neural Networks (ANNs) have been successfully applied to Optical Character Recognition (OCR) yielding excellent results. In this research a technique is presented that segments difficult printed and cursive handwriting, and then classifies the segmented characters. A conventional algorithm is used for the initial segmentation of the words, while an ANN is used to verify whether an accurate segmentation point has been found. After all segmentation points have been detected another ANN is used to identify the characters which remain following the segmentation process [1]. The segmentation process is shown in Figure 1.

Figure 1. Segmentation Process

Further information about the segmentation stage of our system can be found here

2. Neural Based Dictionary

After our segmentation technique has created a set of segregated characters, another Neural Network is used to classify the characters. After classification, these characters are then presented to a neural based dictionary of words. The network used is based on the Hamming network. Its architecture includes one input and one output layer which are both fully interconnected. The input layer accepts ASCII values (divided by 100) of recognised characters which together comprise full words. Each neuron in the output layer points to a word stored in the dictionary [2]. Figure 2 displays the neural based dictionary. Table 1, shows results obtained using the segmentation process and the neural based dictionary.

Figure 2. Neural based Dictionary

Table 1. Results using neural based dictionary

References

[1] M. Blumenstein, and B. Verma, "A Neural Based Segmentation and Recognition Technique for Handwritten Words", World Congress on Computational Intelligence (WCCI '98), Anchorage, Alaska. 1738-1742.

[2] B. Verma, M. Blumenstein, and S. Kukarni, "Recent Achievements in Off-line Handwriting Recognition Systems", International Conference on Computational Intelligence and Multimedia Applications (ICCIMA '98), Melbourne, Australia, 27-33.