Offline Recognition Of Handwritten Text Using Combination Of Neural Networks

Recognition of handwriting is a critical challenge in artificial intelligence having implications for numerous areas such as document analysis, optical character recognition, including the processing of natural languages. In this study, a novel deep neural network technique for offline handwritten textual data recognition is described. Due to the volume of data available today and the numerous algorithmic improvements ongoing, it is now simpler to construct deep neural networks. Recent advancements in GPU accessibility and numerous cloud-based solutions like AWS or Amazon Web Services, as well as Google's cloud computing platform that offer resources to do so have increased the processing power that is required to simulate neural networks. It has shown great promise to achieve high accuracy in handwriting recognition tasks by incorporating a few deep learning approaches, including Region-based Convolutional Neural Networks (RCNN), Convolutional Neural Networks (CNN) along with Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The Connectionist Temporal Classification (CTC) method is also frequently used to train deep learning models for sequence identification and loss estimation.