Abstract
Deep learning (DL) is a hot topic in current pattern recognition and machine learning. DL has unprecedented potential to solve many complex machine learning problems and is clearly attractive in the framework of mobile devices. The availability of powerful pattern recognition tools creates tremendous opportunities for next-generation smart applications. A convolutional neural network (CNN) enables data-driven learning and extraction of highly representative, hierarchical image features from appropriate training data. However, for some data sets, the CNN classification method needs adjustments in its structure and parameters. Mobile computing has certain requirements for running time and network weight of the neural network. In this paper, we first design an image processing module for a mobile device based on the characteristics of a CNN. Then, we describe how to use the mobile to collect data, process the data, and construct the data set. Finally, considering the computing environment and data characteristics of mobile devices, we propose a lightweight network structure for optical character recognition (OCR) on specific data sets. The proposed method using a CNN has been validated by comparison with the results of existing methods, used for optical character recognition.
Highlights
Optical character recognition technology refers to the process of using electronic devices to scan printed characters, determine their shape by detecting edge information, and translate the shapes into computer characters by character recognition [1]
Before introducing mobile computing, let us talk about the research status of the Internet of Things (IoT) and briefly introduce the principle of convolutional neural networks and their application in optical character recognition (OCR); let us talk about the development status of deep learning on mobile devices
We detail the main content of Shui character recognition, which is divided into two parts: first, we describe the construction process of the data set used in the study; we build the convolutional neural network (CNN) model and explain its details
Summary
Optical character recognition technology refers to the process of using electronic devices to scan printed characters, determine their shape by detecting edge information, and translate the shapes into computer characters by character recognition [1]. Deep learning improves the performance of existing mobile multimedia applications, and paves the way for more complex applications for mobile devices. Many of these devices (including smart watches, smartphones, and smart cameras) can perform some sensing and processing, making them smart objects that can learn. Current deep learning technology has made a major breakthrough in the field of OCR, the computing and storage resources of mobile intelligent devices are limited, and the convolutional neural network model usually has hundreds of megabytes of parameters, which makes it difficult to implement in mobile devices. The first research work is to design a neural network structure suitable for mobile devices to enable the recognition of Shui characters. The proposed system will split the character recognition task between the edge device (physically close to the user) and the server (typically located in a remote cloud)
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