The field of document image processing and text synthesis is witnessing significant advancements, with a focus on developing innovative models that can effectively address complex tasks such as scene text synthesis, document dewarping, and image enhancement. Researchers are exploring the potential of diffusion models and generative paradigms to improve the accuracy and efficiency of these tasks. Notably, the integration of multilingual capabilities and the use of synthetic data are emerging as key trends in this area.

One of the primary challenges being addressed is the ability to preserve document structures and ensure high-fidelity text synthesis. To this end, novel architectures and training strategies are being proposed, which enable efficient and robust processing of document images.

Some noteworthy papers in this area include: TextFlux, which introduces an OCR-free DiT model for high-fidelity multilingual scene text synthesis, offering strong multilingual scalability and streamlined training setup. DvD, which proposes a generative paradigm for document dewarping via a coordinates-based diffusion model, achieving state-of-the-art performance with acceptable computational efficiency. GL-PGENet, which presents a parameterized generation framework for robust document image enhancement, ensuring both efficiency and robustness in real-world scenarios. Neural Restoration of Greening Defects, which offers a novel approach for the automatic removal of greening color defects in digitized autochrome photographs based on purely synthetic data. TextSR, which introduces a multimodal diffusion model specifically designed for multilingual scene text image super-resolution, leveraging OCR guidance to enhance fine details within the text and improve overall legibility.