A review study on Image Processing using Machine Learning

Abstract

The paper is primarily about the problem of demonstrating mathematical methods and the algorithm required. To earn their recognition. A lot of attention has been given to the use of handwritten mathematical symbols and equations. Reinforcing the field of pattern recognition. A new and advanced algorithm for identification has been invented .A data set of handwritten digits is emerging as handwritten characters become more diverse. Nevertheless, the issue .The issue lies in the conduct of those handwritten data sets. To comprehend the drawbacks of handwritten digit data sets with diverse numbers. Our handwritten digit representation model is more sophisticated because of the feature's inability to compute. Learning (MIL) involves storing digit data from different feature spaces in a bag. In science and technology, agriculture, and biological imaging, image processing is a prominent topic. Processing, face/iris/image recognition,  and various other areas. Enhancing images is the aim of image processing. Image information can be compressed using it, while in machine learning it is used to optimize differentiable features. Adjusting parameters is necessary to minimize a certain loss or cost function. So , A result has been achieved by the combination of these two. A better grasp of image recognition and processing is necessary. There are numerous fields and uses available. High-tech frameworks that analyze images have the potential to be very beneficial. Agricultural areas can benefit from these uses. Image recognition , among other things. This study presents several methods for recognizing off-line patterns using various machine learning algorithms. Numerous machine learning methods, include support vector machines, convolutional neural networks, multilayer perception, and many others. Finding the most efficient and successful approach for pattern recognition is the primary goal. The study demonstrates how the accuracy of various classification methods varies. Frame works benefit the community and improve quality of life. Extraction and machine learning algorithms provide a viable approach to creating such a system. For example, the Google Cloud Vision API makes it simple for developers to comprehend the content of an image by encapsulating strong machine learning models. This paper discusses some machine learning framework-based image processing research projects. The methods of machine learning are typically used in the process of identifying various numbers and symbols. A segment binary image passes a "rough" classification for the first initialization of the symbols by the Bayesian Network Neural Networks are also utilized for content-based categorization.