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Neural Networks for handwriting recognition
Engineering and Construction
Pages 16 (4016 words)
NEURAL NETWORKS FOR HANDWRITING RECOGNITION Name Date Table of Contents Artificial Neural Networks (ANNs) 6 Neural Network Recognizer 7 Recurrent Neural Networks 7 Long Short-Term Memory (LSTM) 8 Bidirectional Recurrent Neural Networks 9 Connectionist Temporal Classification (CTC) 9 Training the Individual Nets 10 ANNs for Handwriting Recognition 12 Types of ANN for Handwriting Recognition 13 Methods for ANN Based Handwriting Recognition 14 Bidirectional Long Short-Term Memory 14 Integration with an External Grammar 16 ANN Features Extraction 17 Future Direction 17 Conclusion 18 References 19 Executive Summary In the past few years, with the increasing use of information technology based sy
In fact, a large number of researches have forecasted that in future billions of mobile and wireless systems will integrate handwriting recognition facilities. However, it is straightforward and uncomplicated to recognize handwriting when it appears in the form of isolated handwritten symbols as compared to un-segmented linked handwriting (with unidentified initial stages and ends of particular letters). Though, whatever the case is, we need excellent and high speed algorithmic capabilities (Ciresan et al., 2012; Schmidhuber, 2010). In addition, there are many scenarios where conventional techniques of computer vision and digital machine learning are not able to replace human capabilities, for example identification of traffic signs and handwritten digits. ...
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