Design of Optical Character Recognition Using Neural Network

Rakhi Dua

ISBN 10: 6207473973 / ISBN 13: 9786207473977

Editore: LAP LAMBERT Academic Publishing Mär 2024, 2024

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Neuware -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 Once trained, these models can be deployed in diverse applications, from digitizing historical documents to enhancing accessibility for visually impaired individuals, marking a significant advancement in information processing and accessibility.Books on Demand GmbH, Überseering 33, 22297 Hamburg 92 pp. Englisch.

Codice articolo 9786207473977

Dati bibliografici

• Titolo: Design of Optical Character Recognition Using Neural Network
• Editore: LAP LAMBERT Academic Publishing Mär 2024
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 6207473973
• ISBN 13: 9786207473977
• Rilegatura: Taschenbuch
• Condizione: Neu

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Riassunto

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 Once trained, these models can be deployed in diverse applications, from digitizing historical documents to enhancing accessibility for visually impaired individuals, marking a significant advancement in information processing and accessibility.