Open AccessHandwriting to Text Conversion for English Language Using Deep Learning
Author(s) -
Ketaki G. Dhotre,
Harshali K. Ghumate,
Mayuri Mane,
Prof. Savita Lade
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40876
Subject(s) - handwriting , computer science , paragraph , convolutional neural network , handwriting recognition , cursive , artificial intelligence , process (computing) , intelligent character recognition , artificial neural network , deep learning , speech recognition , natural language processing , pattern recognition (psychology) , image (mathematics) , feature extraction , world wide web , character recognition , operating system
Because of the rising use of digital technology in all businesses and in all day-to-day activities to store and communicate information, recognition systems in writing have become a prominent study topic and development. Humans still require handwriting copies to be converted into digital copies that can be shared and preserved electronically. Handwriting recognition is one of the most active study areas, and deep neural networks are being used in it. Humans find it simple to recognise handwriting, but computers find it tough. Nowadays, technologies that detect handwriting letters, characters, and figures assist people in doing more sophisticated activities that would otherwise take a long time and be costly. The purpose of this project is to turn handwritten notes into typed documents. We aim to transform handwritten English characters into a computer-readable format using a paragraph as an input, process the paragraph with cursive writing and symbols support, and then train a neural network algorithm to recognize and display the text. CNN is the neural network model that we used. The image can be uploaded by the user. To eliminate background noise, the system pre-processes the input. The machine then looks for text sections in the image. The system then displays the text that is contained in the image to the user. To conduct horizontalvertical segmentation, we used OpenCV. Keywords: Handwriting, Bi-LSTM, Convolutional Neural Network, Text Conversion, Deep Learning, OpenCV