Open AccessImprove OCR Accuracy with Advanced Image Preprocessing using Machine Learning with Python
Author(s) -
K. L. Handa,
Маниша Шарма,
Rishika Jaiswal,
Poninder Kumar
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g5745.059720
Subject(s) - optical character recognition , computer science , python (programming language) , preprocessor , artificial intelligence , artificial neural network , matlab , scanner , pattern recognition (psychology) , computer vision , speech recognition , natural language processing , image (mathematics) , machine learning , programming language
Optical Character Recognition or Optical Character Reader (OCR) is a pattern-based method consciousness that transforms the concept of electronic conversion of images of handwritten text or printed text in a text compiled. Equipment or tools used for that purpose are cameras and apartment scanners. Handwritten text is scanned using a scanner. The image of the scrutinized document is processed using the program. Identification of manuscripts is difficult compared to other western language texts. In our proposed work we will accept the challenge of identifying letters and letters and working to achieve the same. Image Preprocessing techniques can effectively improve the accuracy of an OCR engine. The goal is to design and implement a machine with a learning machine and Python that is best to work with more accurate than OCR's pre-built machines with unique technologies such as MatLab, Artificial Intelligence, Neural networks, etc.