Handwritten Digit Recognition using Machine and Deep Learning Algorithms Ritik Dixit Rishika Kushwah Samay Pashine Computer Science and Engineering Computer Science and Engineering Computer Science and Engineering Acropolis Institute of Technology & Research Acropolis Institute of Technology & Research Acropolis Institute of Technology & Research Indore, India Indore, India Indore, India [email protected] [email protected] [email protected] Abstract—The reliance of humans over machines has never are not suitable for real-world applications. Ex- For an been so high such that from object classification in photographs automated bank cheque processing system where the system to adding sound to silent movies everything can be performed recognizes the amount and date on the check, high accuracy with the help of deep learning and machine learning algorithms. Likewise, Handwritten text recognition is one of the significant is very critical. If the system incorrectly recognizes a digit, it areas of research and development with a streaming number can lead to major damage which is not desirable. That’s why of possibilities that could be attained. Handwriting recognition an algorithm with high accuracy is required in these real- (HWR), also known as Handwritten Text Recognition (HTR), world applications. Hence, we are providing a comparison is the ability of a computer to receive and interpret intelligible of different algorithms based on their accuracy so that the handwritten input from sources such as paper documents, photographs, touch-screens and other devices [1]. Apparently, most accurate algorithm with the least chances of errors can in this paper, we have performed handwritten digit recognition be employed in various applications of handwritten digit with the help of MNIST datasets using Support Vector Machines recognition. (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models. Our main objective is to compare the This paper provides a reasonable understanding of machine accuracy of the models stated above along with their execution time to get the best possible model for digit recognition. learning and deep learning algorithms like SVM, CNN, and MLP for handwritten digit recognition. It furthermore gives Keywords: Deep Learning, Machine Learning, Handwritten you the information about which algorithm is efficient in Digit Recognition, MNIST datasets, Support Vector Machines performing the task of digit recognition. In further sections (SVM), Multi-Layered Perceptron (MLP), and Convolution Neu- of this paper, we will be discussing the related work that ral Network (CNN). has been done in this field followed by the methodology and implementation of all the three algorithms for the fairer I. INTRODUCTION understanding of them. Next, it presents the conclusion Handwritten digit recognition is the ability of a computer and result bolstered by the work we have done in this to recognize the human handwritten digits from different paper. Moreover, it will also give you some potential future sources like images, papers, touch screens, etc, and classify enhancements that can be done in this field. The last section them into 10 predefined classes (0-9). This has been a of this paper contains citations and references used. topic of boundless-research in the field of deep learning. Digit recognition has many applications like number plate recognition, postal mail sorting, bank check processing, etc II. RELATED WORK arXiv:2106.12614v1 [cs.CV] 23 Jun 2021 [2]. In Handwritten digit recognition, we face many challenges With the humanization of machines, there has been a because of different styles of writing of different peoples as it substantial amount of research and development work that is not an Optical character recognition. This research provides has given a surge to deep learning and machine learning a comprehensive comparison between different machine along with artificial intelligence. With time, machines are learning and deep learning algorithms for the purpose of getting more and more sophisticated, from calculating the handwritten digit recognition. For this, we have used Support basic sums to doing retina recognition they have made our Vector Machine, Multilayer Perceptron, and Convolutional lives more secure and manageable. Likewise, handwritten Neural Network. The comparison between these algorithms text recognition is an important application of deep learning is carried out on the basis of their accuracy, errors, and and machine learning which is helpful in detecting forgeries testing-training time corroborated by plots and charts that and a wide range of research has already been done that have been constructed using matplotlib for visualization. encompasses a comprehensive study and implementation of various popular algorithms like works done by S M The accuracy of any model is paramount as more accurate Shamim [3], Anuj Dutt [4], Norhidayu binti [5] and Hongkai models make better decisions. The models with low accuracy Wang [8] to compare the different models of CNN with the fundamental machine learning algorithms on different III. METHODOLOGY grounds like performance rate, execution time, complexity The comparison of the algorithms (Support vector and so on to assess each algorithm explicitly. [3] concluded machines, Multi-layered perceptron and Convolutional neural that the Multilayer Perceptron classifier gave the most network) is based on the characteristic chart of each algorithm accurate results with minimum error rate followed by Support on common grounds like dataset, the number of epochs, Vector Machine, Random Forest Algorithm, Bayes Net, Na¨ıve complexity of the algorithm, accuracy of each algorithm, Bayes, j48, and Random Tree respectively while [4] presented specification of the device (Ubuntu 20.04 LTS, i5 7th gen a comparison between SVM, CNN, KNN, RFC and were processor) used to execute the program and runtime of the able to achieve the highest accuracy of 98.72% using CNN algorithm, under ideal condition. (which took maximum execution time) and lowest accuracy using RFC. [5] did the detailed study-comparison on SVM, KNN and MLP models to classify the handwritten text and A. DATASET concluded that KNN and SVM predict all the classes of dataset correctly with 99.26% accuracy but the thing process Handwritten character recognition is an expansive research goes little complicated with MLP when it was having trouble area that already contains detailed ways of implementation classifying number 9, for which the authors suggested to which include major learning datasets, popular algorithms, use CNN with Keras to improve the classification. While features scaling and feature extraction methods. MNIST [8] has focused on comparing deep learning methods with dataset (Modified National Institute of Standards and machine learning methods and comparing their characteristics Technology database) is the subset of the NIST dataset to know which is better for classifying mediastinal lymph which is a combination of two of NIST’s databases: Special node metastasis of non-small cell lung cancer from 18 F-FDG Database 1 and Special Database 3. Special Database 1 and PET/CT images and also to compare the discriminative Special Database 3 consist of digits written by high school power of the recently popular PET/CT texture features students and employees of the United States Census Bureau, with the widely used diagnostic features. It concluded respectively. MNIST contains a total of 70,000 handwritten that the performance of CNN is not significantly different digit images (60,000 - training set and 10,000 - test set) in from the best classical methods and human doctors for 28x28 pixel bounding box and anti-aliased. All these images classifying mediastinal lymph node metastasis of NSCLC have corresponding Y values which apprises what the digit from PET/CT images. However, CNN does not make use of is. the import diagnostic features, which have been proved more discriminative than the texture features for classifying small- sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research. All we need is lots of data and information and we will be able to train a big neural net to do what we want, so a convolution can be understood as ”looking at functions surrounding to make a precise prognosis of its outcome.” [6], [7] has used a convolution neural network for handwritten digit recognition using MNIST datasets. [6] has used 7 layered CNN model with 5 hidden layers along with gradient descent and back prorogation model to find and compare the accuracy on different epochs, thereby getting maximum accuracy of 99.2% while in [7], they have briefly discussed Figure 1. Bar graph illustrating the MNIST handwritten digit training dataset different components of CNN, its advancement from LeNet-5 (Label vs Total number of training samples). to SENet and comparisons between different model like AlexNet, DenseNet and ResNet. The research outputs the LeNet-5 and LeNet-5 (with distortion) achieved test error rate of 0.95% and 0.8% respectively on MNIST data set, the architecture and accuracy rate of AlexNet is same as LeNet-5 but much bigger with around 4096000 parameters and ”Squeeze-and-Excitation network” (SENet) have become the winner of ILSVRC-2017 since they have reduced the top-5 error rate to 2.25% and by far the most sophisticated model of CNN in existence. Figure 2. Plotting of some random MNIST Handwritten digits. B. SUPPORT VECTOR MACHINE D. CONVOLUTIONAL NEURAL NETWORK Support Vector Machine (SVM) is a supervised machine learning algorithm. CNN is a deep learning algorithm that is widely used for image recognition In this, we generally plot data items in n-dimensional space where n is the and classification. It is a class of deep neural networks that require minimum number of features, a particular coordinate represents the value of a feature, pre-processing. It inputs the image in the form of small chunks rather we perform the classification by finding the hyperplane that distinguishes the than inputting a single pixel at a time, so the network can detect uncertain two classes. It will choose the hyperplane that separates the classes correctly.