Segmentation Free Approach Using Hybrid Network Model for Optical Character Recognition
Keywords:
Optical Character Recognition (OCR), Long Short-term memory (LSTM), Multi-Layer perceptron (MLP), Convolutional Neural Network (CNN).Abstract
Optical Character Recognition (OCR) have an importance in the research based on image processing and recognizing the pattern. It serves as an automatic technique for identifying the various patterns in diverse applications. The recognition technique effectively explores the characters, images, and even the handwriting of an individual. Much research concerning OCR involves every deep learning, machine learning, and artificial intelligence algorithm. A segmentation-free approach has been introduced in this paper that combines with Hybrid Network Model (HNM) that works on collaborating convolutional neural network and recurrent neural network for minimizing the time in processing and improving the accuracy. In hybrid model we consider CNN in the input layer, the middle layer is LSTM and MLP is the layer generated as an output. The length of sequences in the input and output can vary it need not be specific they are managed by the encoder and decoders present in LSTM.
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