![]() Furthermore, we review the state-of-the-art OCR applications in healtcare informatics. We analyze the accuracy and reliability of the OCR packages employing a dataset including 1227 images from 15 different categories. ![]() In this work, several qualitative and quantitative experimental evaluations have been performed using four well-know OCR services, including Google Docs OCR, Tesseract, ABBYY FineReader, and Transym. A set of different OCR platforms are now available which, aside from lending theoretical contributions to other practical fields, have demonstrated successful applications in real-world problems. With the help of OCR systems, we have been able to save a reasonable amount of effort in creating, processing, and saving electronic documents, adapting them to different purposes. The rapid generation of digital images on a daily basis prioritizes OCR as an imperative and foundational tool for data analysis. Optical character recognition (OCR) as a classic machine learning challenge has been a longstanding topic in a variety of applications in healthcare, education, insurance, and legal industries to convert different types of electronic documents, such as scanned documents, digital images, and PDF files into fully editable and searchable text data. Paper reviews methodologies with respect to the phases of character recognition. This paper presents review of work to recognize off-line handwritten text for various Indian language scripts. One can trace extensive work for off-line handwritten recognition for English and Arabic script. Many researchers have presented their work and many algorithms are proposed to recognize handwritten and printed characters. Use of paper to write handwritten text, converting to an image using scanner, identifying handwritten characters from the image is known as off-line handwritten text recognition is a challenging area due to the fact that different people will have different style of writing and all scripts have their own character set and complexities to write text. As technology has advanced tablet and many similar devices allows humans to input data in form of handwriting. In past collecting, storing and transmitting information in form of handwritten script was the most convenient way and is still prevailing as a convenient medium in the era of digital technology. Handwritten recognition is an area of research where many researchers have presented their work and is still an area under research to achieve higher accuracy. ![]() This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. HCR is useful in cheque processing in banks almost all kind of form processing systems, handwritten postal address resolution and many more. ![]() Research in OCR is popular for its application potential in banks, post offices, office automation etc. It is one of the most successful applications of automatic pattern recognition. Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields.
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