IMPLEMENTATION OF DEEP LEARNING ALGORITHM IN HANDWRITING TO TEXT DOCUMENT CONVERSION APPLICATION
Keywords:
Handwriting, recognition, Tesseract.js, OCR, Web Application, DigitalizationAbstract
The development of information and communication technology has driven the need for systems capable of efficiently converting handwritten text into digital text. This study aims to develop a web-based application capable of real-time handwriting recognition using Tesseract.js, a JavaScript library for optical character recognition (OCR). The application is designed to assist users in converting handwritten documents into editable text formats, thereby enhancing productivity and information accessibility.
The methods used in this study include uploading handwritten images, preprocessing the images to improve input quality, and applying OCR algorithms using Tesseract.js to recognize characters and words. The recognized results are then displayed on the user interface, with an option for manual correction if needed. The study also evaluates the accuracy of the text recognition produced by the application by comparing the recognition results with the original text.
The results show that the developed application is capable of recognizing handwriting with a satisfactory level of accuracy, despite variations in handwriting styles. This application is expected to make a significant contribution in the field of document digitization and data processing, and serve as a reference for the development of similar systems in the future.
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