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https://doi.org/10.62836/jcmea.v3i1.030103
Xiong, S., Chen, X., & Zhang , H. . (2023). Deep Learning-Based Multifunctional End-to-End Model for Optical Character Classification and Denoising. Journal of Computational Methods in Engineering Applications, 3(1), 1–13. https://doi.org/10.62836/jcmea.v3i1.030103
Volume 3, Issue 1, 2023

Deep Learning-Based Multifunctional End-to-End Model for Optical Character Classification and Denoising

Optical Character Recognition (OCR) has revolutionized document processing by converting scanned documents, PDFs, and images captured by cameras into editable and searchable text. This technology is crucial for digitizing historical documents, streamlining data entry processes, and improving accessibility for the visually impaired through text-to-speech technologies. Despite its widespread application, OCR faces significant challenges, especially in accurately recognizing text in noisy or degraded images. Traditionally, OCR systems have treated noise reduction and character classification as separate stages, which can compromise the overall effectiveness of text recognition. Our research introduces a groundbreaking Multifunctional End-to-End Model for Optical Character Classification and Denoising, which integrates these functions within a unified framework. By employing a dual-output autoencoder, our model concurrently denoises images and recognizes characters, thereby enhancing both the efficiency and accuracy of OCR. This paper outlines the model's development and implementation, explores the interplay between denoising and classification, and presents compelling experimental results that demonstrate marked improvements over conventional OCR methods.

component; Optical Character Classification; denoising; autoencoder; deep learning

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