Knowledge Extraction in Digit Recognition Using MNIST Dataset: Evolution in Handwriting Analysis

Knowledge Extraction in Digit Recognition Using MNIST Dataset: Evolution in Handwriting Analysis

Rohit Rastogi (ABES Engineering College, Ghaziabad, India), Himanshu Upadhyay (ABES Engineering College, Ghaziabad, India), Akshit Rajan Rastogi (ABES Engineering College, Ghaziabad, India), Divya Sharma (ABES Engineering College, Ghaziabad, India), Prankur Bishnoi (ABES Engineering College, Ghaziabad, India), Ankit Kumar (ABES Engineering College, Ghaziabad, India) and Abhinav Tyagi (ABES Engineering College, Ghaziabad, India)
Copyright: © 2021 |Pages: 24
DOI: 10.4018/IJKM.2021100103
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Abstract

In handwriting recognition, traditional systems have relied heavily on handcrafted features and a massive amount of prior data and knowledge. Deep learning techniques have been the focus of research in the field of handwriting digit recognition and have achieved breakthrough performance in the last few years for knowledge extraction and management. KM and knowledge pyramid helps the project with its relationship with big data and IoT. The layers were selected randomly by which the performance of all the cases was found different. Data layers of the knowledge pyramid are formed by the sensors and input devices, whereas knowledge layers are the result of knowledge extraction applied on data layers. The knowledge pyramid and KM helps in making the use of IoT and big data easily. In this manuscript, the knowledge management principles capture the handwritten gestures numerically and get it recognized correctly by the software. The application of AI and DNN has increased the acceptability significantly. The accuracy is better than other available software on the market.
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Introduction

Handwriting recognition is a process, which has been a centre of attraction for analysts since 1998, with nearly all the calculations structured at a point. In this process, the error rate obtained by direct classifier was 12% in 1998, which gradually decreased to 0.23% in 2012 when applied by convolution nets. Nowadays, as there is a drastic increase in the amount of information, Researchers and AI specialists are trying their level best to create and approve solo learning strategies, for example, auto-encoders, profound learning models, etc.

In this research work, a handwriting recognition framework is actualized with the celebrated MNIST informational index. This index is a well known dataset. It consists of a large database, which is required for training and assessment of various image processing systems. Machine learning is one of the fields where this data is regularly used in order to test and train various systems. By using KM and converting all the data achieved to knowledge it can help in solving many other problems.

Digit recognition system (DRS) is a subfield of image processing that is used to scan the image or documents with the help of the MNIST database or dataset. The main goal of this research work is to implement a project and discuss its accuracy which scans the image digits using basic image correlation, also known as matrix matching. The six cases performed different outputs as shown in subsequent sections, all the cases are different from each other which show different accuracy and different performances. Still, the rapid and high growth of the handwritten numerical data generated recently and rising availability of massive processing power urge for improvements in recognition accuracy and deserves much needed further investigation (Hamad, K. et al., 2016).

Convolutional neural networks (CNNs) are found to be very effective in recognizing the structure of handwritten digits/characters in many ways, which also helps in automatically finding distinct features. This scenario makes CNN the best available solution for solving handwritten digit recognition problems. We have introduced a suitable combination of learning parameters in designing a CNN that helps us achieve a new absolute record in classifying MNIST handwritten digits. Knowledge obtained can help in further training machines and making predictions and reducing computation required in understanding the whole image.

Research Objective

Digit recognition systems are being used on a very large scale in today's world because the handwritten digits are not always in the correct size, shape or style. That's why, a lot of problems may occur to find the correct digit in the real world with the help of a machine (Dorosh, N. et al., 2020).

When we include ensemble architecture, it helps in increasing the network’s recognition accuracy. So here, the objective is to accomplish comparable accuracy by using a pure CNN architecture without using ensemble architecture. As, ensemble architecture introduces computational cost and high testing complexity. Therefore, in order to acquire accuracy even better than ensemble architecture, we are trying CNN architecture, with additional bonus profit of reduced operational complexity and cost.

Identification and Motivation of Issue

This research work gives us the motivation to create a model using CNN (Convolution Neural Network) that would be able to receive and identify digits in the form of images. Also, the goal of the program is to learn about CNN, and apply it to handwriting recognition systems (Dorosh, N. et al., 2020). The aim of this research paper is to find the accuracy of CNN to classify handwritten digits using multiple hidden layers.

Our motive in this research work is to explore the various design possibilities like kernel size, receptive field, and stride size, number of layers, padding and dilution for CNN-based handwritten digit recognition.

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