DeepQ Feature Selection and Recognition of Handwritten Prediction Using Convolutional Neural Networks

Abstract

Purpose: Deep learning (DL) is referred to as the "hot subject" in pattern recognition and machine learning. The unmatched potential of deep learning allows for the resolution of the majority of complex machine learning issues, and it is evident that it will receive attention within the framework for mobile devices. There are tools for pattern recognition that can be used by smart applications of the next generation to make huge changes. Design/Methodology/Approach: The deep feature convolutional network (DEEPQ-CNN) extracts the high representation and features of the hierarchical image from the relevant training data when data-driven learning is enabled. In addition, the DEEPQ-CNN characterization strategies are adapted to a couple of datasets in the boundaries and construction. The running time of the neural network and the network's weight are essential requirements for mobile computing. Finding/Results: In the proposed system, the design of the image processing module is based on the characteristics of a mobile device's convolutional neural network. However, the use of mobile devices for data collection, processing, and construction is described. Last but not least, the computing conditions and mobile device data features are taken into account. Original Value: For optical character recognition (OCR), specific datasets support the lightweight network structure. CNN is utilized to approve the proposed framework when examinations are made with the results of past methods to perceive the optical person. Paper Type: Research