Author(s): Jhankar Moolchandani and Dr. Kulvinder Hundal

Abstract: The technology of Document Analysis and Recognition, as a branch of pattern recognition, faces various practical demands in the real world, such as the digitization of books, newspapers, and archives, invoicing, and corporate documents. Pattern recognition and machine learning are among the most cutting-edge areas in software science. Statistical learning theory based neural network approaches and methodologies have recently come under more and more scrutiny. By utilizing the proper character recognition and segmentation modules for optical character recognition (OCR), it is essential to identify the document's language and printing style. In the field of pattern recognition, recognizing handwritten papers is a difficult problem. Algorithms and statistical models that computers use to complete a certain task without being explicitly focused on machine learning (ML). It is possible to utilize these algorithms for a variety of purposes, including data mining and image processing. It is easy to automate tasks by utilizing machine learning when an algorithm has learned how to deal with data. The purpose of this review article is to summarize and compare many well-known methodologies that are utilized at various phases of a pattern recognition system's development. After examining several strategies for pattern identification, it was determined that the most accurate method is optical character recognition (OCR). Optical character recognition (OCR) scanners, on the other hand, have a 99 percent accuracy rate. Diabetes Retinopathy (DR) also has the lowest accuracy, at just percent.

DOI: 10.33545/27076571.2022.v3.i2a.52

Pages: 24-29 | Views: 2095 | Downloads: 1128

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