ROBDD-TrOCRBERTa: a novel robust-optimized blurred document text deblurring and completion with DCGAN-TrOCR and DistilRoBERTa

Blurred text documents such as historical documents, handwritten manuscripts, old newspapers, moist invoices or legal agreements, old books, hand written notes often present readability challenges because the quality of the text has deteriorated over time. The proposed Robust Optimized Blurred Docum...

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Vydáno v:International journal of information technology (Singapore. Online) Ročník 16; číslo 7; s. 4611 - 4619
Hlavní autoři: Ranjan, Arti, Ravinder, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.10.2024
Springer Nature B.V
Témata:
Accuracy
Artificial Intelligence
Blurring
Computer Imaging
Computer Science
Computer vision
Datasets
Deep learning
Dictionaries
Diffusion models
Documents
Handwriting
Image Processing and Computer Vision
Image quality
International conferences
Machine Learning
Natural language processing
Original Research
Pattern recognition
Pattern Recognition and Graphics
Readability
Recognition
Robustness (mathematics)
Software Engineering
Vision
Robust-optimized blurred document deblurring
ROBDD-TrOCRBERTa
TrOCR
DistilRoBERTa
Text deblurring
NLP-based post-processing
ISSN:2511-2104, 2511-2112
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Popis
Shrnutí:Blurred text documents such as historical documents, handwritten manuscripts, old newspapers, moist invoices or legal agreements, old books, hand written notes often present readability challenges because the quality of the text has deteriorated over time. The proposed Robust Optimized Blurred Document Text Deblurring and Text Recognition (ROBDD-TrOCRBERTa) method tackles the challenge of improving readability in deteriorated text documents such as historical documents, handwritten manuscripts, and old newspapers. This innovative approach is divided into two phases. First, it employs Deblurring using DCGAN to enhance image quality by reducing noise and blurriness. Subsequently, it leverages TrOCR integrated with DistilRoBERTa for efficient text recognition and completion. Experimental results show that this method is effective in various real-world scenarios, making it a promising solution for automated document analysis and digitization in challenging conditions.
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-024-02073-9