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A convolution neural network with encoder-decoder applied to the study of Bengali letters classification. Experimental results show that the images' augmentation helps the model train better and improves its accuracy and loss.Ĭitation: Sayed Mohsin Reza, Md Al Masum Bhuiyan, Nishat Tasnim. The results suggest that CNN with encoder-decoder can recognize complex grapheme characters with higher precision than traditional CNN. We introduce regularization techniques to reduce the over-fitting in the fully connected layers. A large number of variations make additional complexity in recognition and may lead the model into over-fitting or under-fitting. In this study, almost 200,840 grapheme images were analyzed, including root, vowels, and consonants. The key idea is to encode images by convolution and decode them by deconvolution so that the max-pooling and up-sampling layers can correctly identify grapheme pixel-by-pixel.
We use several serial non-linear layers as the encoders and a corresponding set of decoders that work as a pixel-wise classifier for letter recognition.
A class of deep Convolutional Neural Networks (CNNs) with encoder-decoder is used to classify handwritten letters. Having set a bigger scope, this paper deals with the recognition of Bengali handwritten script letters. Bengali is now the fifth most spoken native language and the seventh most spoken language by the total number of speakers in the world. Bengali grapheme classification is a complex task as it has 49 letters and 18 potential diacritics with almost 13,000 possible variations. Handwritten grapheme recognition is popular research in computer vision and now widespread in the commercial industry due to its large number of applications in document analysis and recognition.