Abstract
The paper presents an overview of a new hybrid approach for Mizo optical character recognition. The preprocessing enhances the accuracy of recognition by removing noise. After this image enhancement, a hybrid approach character segmentation using bounding box and morphological dilation is performed which merges the isolated blobs of Mizo character into a single entity. In feature extraction, another hybrid method is derived using zoning and topological feature which enhances multi-font and multi-size character recognition. The segmented characters are subdivided into nine equal zones and from each zone, four topological features are extracted, forming a total of 9 × 4 = 36 features for each character. These features are used for classification and recognition. An experiment is carried out in 24 different types of fonts using four different types of artificial neural network architecture. Each architecture is compared and analysed. The backpropagation neural network has the highest accuracy, with above 99% rate of recognition.
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Hussain, J., Vanlalruata (2018). A Hybrid Approach Optical Character Recognition for Mizo Using Artificial Neural Network. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_54
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DOI: https://doi.org/10.1007/978-981-10-8633-5_54
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