Abstract
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.
Highlights
Pattern detection and machine learning have faced significant problems due to the discovery of modern technologies and streaming news sources [1]
The following steps were taken before performing the literature review: "Handwritten Digit Recognition," "Handwritten Digit Segmentation," "Handwritten Digit Classification," "Machine Learning Methods," "Deep Learning," "Image Analysis on Text Files," "Support Vector Machine," "Artificial Neural Networks," "Conventional Neural Networks," and "Preprocessing Handwritten Digits."
This section compares support vector machines, artificial neural networks, and classical neural networks to determine the best algorithm for highperformance recognition with a good track record
Summary
Pattern detection and machine learning have faced significant problems due to the discovery of modern technologies and streaming news sources [1]. Handwritten digit recognition is a required functionality in various practical applications, including administration and finance [4]. These companies need a substantial degree of recognition and the highest possible level of dependability. Studies conducted on OCR systems have identified several features for digital handwriting recognition. The majority of parts are standardized, others use unique qualities to increase classification efficiency. This involves graphical methods, shadow-based and gradient-based features [10]. The majority of image pre-processing techniques can suppress noise and restore photos, allowing for easier manipulation of the image and further improving OCR accuracy
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: European Journal of Engineering and Technology Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
