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Optical Character Recognition (OCR) utilizing neural networks is a transformative technology enabling the automatic extraction of text from images. By leveraging deep learning architectures such as Convolutional Neural Networks (CNNs) and recurrent networks like Long Short-Term Memory (LSTM) models, OCR systems can efficiently process complex visual data and accurately transcribe it into machine-readable text. This process involves training the neural network on labeled image-text pairs, enabling it to learn the intricate patterns and structures inherent in characters and words. Through iterative training and validation, the OCR model refines its ability to recognize text across various fonts, languages, and styles. Once trained, these models can be deployed in diverse applications, from digitizing historical documents to enhancing accessibility for visually impaired individuals, marking a significant advancement in information processing and accessibility.
About the author
Dr. Rakhi Dua is B.Tech, M.Tech and PhD in Electronics and Communication Engineering. Her area of expertise is Electromagnetic Theory, Digital Signal Processing, Optical Communication. She has also done M.B.A in Human Resource Management. She has been in teaching from last 15 years and presently working at K.R Mangalam University, Gurgaon, Haryana.
