OCR-Diff: A Two-Stage Deep Learning Framework for Optical Character Recognition Using Diffusion Model in Industrial Internet of Things
Chaewon Park, Vikas Palakonda, Sangseok Yun, Il‐Min Kim, Jae‐Mo Kang · IEEE Internet of Things Journal · 2024
Optical character recognition (OCR) is one of the key enabling technologies in industrial internet-of-things (IIoT) for extracting and utilizing useful textual information, but it is technically challenging due to poor environmental conditions. To deal with such challenges, in this letter, we propose a novel two-stage deep learning framework for OCR using a generative diffusion model, namely, OCR-Diff. In the first stage, our customized conditional U-Net is pre-trained jointly with a feature extractor with the aid of the forward diffusion process such that the quality of a low-resolution text image is improved via the reverse diffusion process. In the next stage, the pre-trained conditional U-Net and feature extractor are jointly fine-tuned for an off-the-shelf text recognizer to precisely recognize the texts in the image. Experimental results on TextZoom datasets substantiate the superiority and effectiveness of the proposed scheme.