JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
A comprehensive survey on Recurrent neural network in Handwritten text Recognition.
Vishrutha B P, Sampath kumar S
Automatic handwriting recognition, abbreviated as AHR, is a method of assessing handwritten language for converting it into machine-readable text. This area of study relies on the advancements in natural language processing, visual analysis, and neural networks to be able to convincingly decode print and cursive scripts as well as other personalized writing styles. AHR systems identify photographs or live data from style-aware devices by employing complex algorithms and massive scale machine learning models. This effort helps in categorizing handwritten phrase and characters and translating them into computers form. Some simple everyday activities such as typing, having files for receipts, medical records, etc. could be simplified with help of this model. Some of the important applications include: scanning of old manuscripts, increase in use of digital records, and improving on the ways that make the information more understandable to the blind and the visually impaired. AHR persists in conducting research and development work to enhance the robustness and expand the ability of such systems to deal with various writing styles.
Character Recognition, Feature Extraction, Text Extraction, Feature matching, OCR, NLP.