Abstract
Handwritten text acknowledgment is yet an open examination issue in the area of Optical Character Recognition (OCR). This paper proposes a productive methodology towards the advancement of handwritten text acknowledgment frameworks. The primary goal of this task is to create AI calculation to empower element and information extraction from records with manually written explanations, with an, expect to distinguish transcribed words on a picture.
 The main aim of this project is to extract text, this text can be handwritten text or it can machine printed text and convert it into computer understandable or wNe can say computer editable format. To implement thais project we have used PyTesseract which is an open-sourcemOCR engine used to recognize handwritten text and OpenCV a library in python used to solve computer vision problems. So the input image is executed in various steps, first there is pre-processing of an image then there is text localization after that there is character segmentation and character recognition and finally we have post-processing of image. Further image processingalgorithms can also be used to deal with the multiple characters input in a single image, tilt image, or rotated image. The prepared framework gives a normal precision of more than 95 % with the concealed test picture.
Highlights
This paper proposes a productive methodology towards the advancement of handwritten text acknowledgment frameworks
Further image processingalgorithms can be used to deal with the multiple characters input in a single image, tilt image, or rotated image
The project is about extraction of transcribed content from a picture, which is an optical acknowledgment of characters is the electronic or mechanical transformation of pictures of composed, manually written, or printed text into machine- encoded text, regardless of whether from a checked archive, a photograph of a record, a scenephotograph or from caption-text superimposed on a picture
Summary
The project is about extraction of transcribed content from a picture, which is an optical acknowledgment of characters is the electronic or mechanical transformation of pictures of composed, manually written, or printed text into machine- encoded text, regardless of whether from a checked archive, a photograph of a record, a scenephotograph (for instance the content on signs and announcements in a scene photograph) or from caption-text superimposed on a picture. Utilized as a type of information section from printed paper information records – regardless of whether identification reports, solicitations, bank explanations, automated receipts, business cards, mail, printouts of static-information, or any appropriate documentation – it is a typical technique for digitizing printed messages with the goal that they can be electronically altered, looked, put away more minimalistically, showed on-line, and utilized in machine cycles, for example, intellectual processing, machine interpretation, (removed) text- todiscourse, key information, and text mining. The primary goal of this task is to create AI calculation to empower element and information extraction from records with manually written explanations, with an expectation to distinguish transcribed words on a picture
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