The automated recognition of previously unseen patterns in digital images by machines is known as Pattern Recognition on Image Processing, i.e., Image Processing Pattern Recognition (IPPR). This technology is widely used in a range of application areas such as: facial recognition, surveillance, medical imaging, biometrics, and automated optical character recognition. IPPR involves various techniques such as feature extraction, segmentation, classification, and clustering. During automated IPPR tasks, the accuracy of the classification process is a key measure that decides the reliability and accuracy of the automated system. Proper feature selection and recognition algorithms along with stringent accuracy parameters are needed for the implementation of robust IPPR systems. Moreover, efficient segmentation and classification algorithms must be used to attain high accuracy and runtime efficiency. Recent studies have shown that ensemble learning methods have attained higher classification accuracy compared to single algorithms. This technology is used across many disciplines including Computer Vision, Machine learning, Artificial Intelligence, Robotics, and Biomedical Image Processing. IPPR algorithms are used not only for accuracy constraints but also for real-time applications. Despite the advances in the accuracy of IPPR algorithms, further improvements are needed in order to enable better accuracy with lower computational time and resources.
A. Vaniprabha1, T. Kiruthiga2, K. Menaka3
SNS College of Engineering, India1, Vetri Vinayaga College of Engineering and Technology, India2, Saveetha Amaravati University, India3
Automated, Pattern, Machine, Character Recognition, System, Artificial Intelligence
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 4 , Issue: 3 , Pages: 480 - 483 )
Date of Publication :
June 2023
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280
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