A Multi-Stage Deep Learning Model for the Enhancement of the Quality of Camera-Captured Document Images
Year: 2025
Type: article
Abstract: This study introduces a multistaged deep learning model aimed at enhancing document images captured through handheld or mobile cameras. The model comprises a modular three-stage pipeline for denoising, deblurring, and enhancement to progressively improve image clarity. Each stage leverages pretrained, task-specific networks to resolve common degradation issues such as sensor noise, motion blur, and uneven illumination. By progressively refining the image through these stages, the model effectively addresses common degradations found in captured image documents. The proposed model was trained and evaluated on camera-captured documents and compared with different existing models, such as GCDRNet and DocEnTr, achieving higher PSNR scores and improving text clarity. The experimental results render the model ideal for OCR and digital archiving use, highlighting its robustness and superior generalization across real-world document conditions.
DOI: 10.48084/etasr.14469
Authors: Pushplata Dubey, D. R. Shashikumar
Institution: Visvesvaraya Technological University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $431
OpenAlex ID: W4417147017