计算机科学
人工智能
数字识别
支持向量机
数字
模式识别(心理学)
机器学习
过程(计算)
排名(信息检索)
弦(物理)
特征提取
分割
特征(语言学)
卷积神经网络
人工神经网络
数学
算术
语言学
哲学
数学物理
操作系统
作者
Hiral Raja,Aarti Gupta,Rohit Miri
出处
期刊:European Journal of Engineering and Technology Research
[European Open Access Publishing (Europa Publishing)]
日期:2021-05-29
卷期号:6 (4): 37-44
被引量:1
标识
DOI:10.24018/ejers.2021.6.4.2460
摘要
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.
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