Machine Learning Techniques for Handwritten Digit Recognition

2020 ◽  
pp. 414-426
Author(s):  
Ahmad Taher Azar ◽  
Alaa Khamis ◽  
Nashwa Ahmad Kamal ◽  
Brian Galli
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Learning Techniques ◽  
Handwritten Digit

scholarly journals Handwritten Digit Recognition Using Machine Learning Algorithms

2018 ◽  
Vol 3 (1) ◽  
pp. 29 ◽  
Author(s):  
S M Shamim ◽  
Mohammad Badrul Alam Miah ◽  
Angona Sarker ◽  
Masud Rana ◽  
Abdullah Al Jobair
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Learning Algorithms ◽  
Data Entry ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The main problem lies within the ability on developing an efficient algorithm that can recognize hand written digits, which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning techniques. The main objective of this paper is to ensure the effectiveness and reliability of the approached recognition of handwritten digits. Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for the recognition of digits using WEKA. The experimental results showed that the highest accuracy was obtained by Multilayer Perceptron with the value of 90.37%.

scholarly journals Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

2021 ◽  
Vol 6 (4) ◽  
pp. 37-44
Author(s):  
Hiral Raja ◽  
Aarti Gupta ◽  
Rohit Miri
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Digit Recognition ◽  
Learning Techniques ◽  
Effective System ◽  
Handwritten Digit ◽  
Digit String ◽  
And Function

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

scholarly journals Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition

2021 ◽  
Vol 21 (1) ◽  
pp. 574
Author(s):  
Owais Mujtaba Khandy ◽  
Samad Dadvandipour
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Character Recognition ◽  
Machine Learning Algorithms ◽  
Maximum Efficiency ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Work Done

<p><span>This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and Random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods. </span></p>

scholarly journals A Survey of Handwritten Character Recognition with MNIST and EMNIST

2019 ◽  
Vol 9 (15) ◽  
pp. 3169 ◽  
Author(s):  
Alejandro Baldominos ◽  
Yago Saez ◽  
Pedro Isasi
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Data Augmentation ◽  
State Of The Art ◽  
Handwritten Digit Recognition ◽  
Test Error ◽  
Digit Recognition ◽  
Original Dataset ◽  
Learning Techniques ◽  
Handwritten Digit

This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have explored the performance of convolutional neural networks (CNNs) and other deep learning techniques over this dataset. To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely related works, omitting most of the literature. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset out-of-the-box. Also, works using CNNs are reported separately; as they are becoming the state-of-the-art approach for solving this problem. Nowadays, a significant amount of works have attained a test error rate smaller than 1% on this dataset; which is becoming non-challenging. By mid-2017, a new dataset was introduced: EMNIST, which involves both digits and letters, with a larger amount of data acquired from a database different than MNIST’s. In this paper, EMNIST is explained and some results are surveyed.

scholarly journals Handwritten Digit Recognition by using Pattern Recognition & Consensus Clustering

2020 ◽  
Vol 9 (4) ◽  
pp. 2263-2267
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Consensus Clustering ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Expert Networks ◽  
Vector Estimation ◽  
The Right ◽  
Incorrect Data

In Big Data, Pattern Recognition and Consensus Clustering procedures have developing significance to the scholastic and expert networks. Today there is an extraordinary worry for ordering the information, as information in wrong classification implies incorrect data, which thus results wastage of resources and hurting the association. Example acknowledgment (PR) helps in maintaining a strategic distance from poor order of information by recognizing the right structure of information in dataset. Perceiving an example is the computerized procedure of finding the specific match and regularities of information, which is firmly identified with Artificial Intelligence and Machine Learning. PR goes about as an essential advance to give bunching since it examinations the structure and vector estimation of every character in dataset. Accord Clustering (CC) additionally called as bunching gatherings, assumes a critical job in arranging and keep up in any sort of information. This is a strategy that joins different bunching answers forget steady, precise and novel outcomes. Right now, actualize PR and CC strategies; we use MNIST dataset which is an enormous database of transcribed digits that is regularly utilized for preparing different frameworks in the field of Machine Learning

scholarly journals Critique of Various Algorithms for Handwritten Digit Recognition Using Azure ML Studio

2020 ◽  
pp. 1-5
Author(s):  
Goutham Cheedella
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Depth Analysis ◽  
Vector Machines ◽  
Handwritten Digit

Handwritten Digit Recognition is probably one of the most exciting works in the field of science and technology as it is a hard task for the machines to recognize the digits which are written by different people. The handwritten digits may not be perfect and also consist of different flavors. And there is a necessity for handwritten digit recognition in many real-time purposes. The widely used MNIST dataset consists of almost 60000 handwritten digits. And to classify these kinds of images, many machine learning algorithms are used. This paper presents an in-depth analysis of accuracies and performances of Support Vector Machines (SVM), Neural Networks (NN), Decision Tree (DT) algorithms using Microsoft Azure ML Studio.

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