Abstract
Recognition of Bangla handwritten compound characters has a significant role in Bangla language in order to develop a complete Bangla OCR. It is a challenging task owing to its high variation in individual writing style and structural resemblance between characters. This paper proposed a novel approach to recognize Bangla handwritten compound characters by combining deep convolutional neural network with Bidirectional long short-term memory (CNN-BiLSTM). The efficacy of the proposed model is evaluated on test set of compound character dataset CMATERdb 3.1.3.3, which consists of 171 distinct character classes. The model has gained 98.50% recognition accuracy which is significantly better than current state-of-the-art techniques on this dataset.
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