关键词: 场景文本图像超分辨率, 潜在结构先验, 扩散模型, 先验提示, 文本识别

Abstract:

Scene Text Image Super-Resolution (STISR) aims to recover clear, complete, and recognizable character structures from Low-Resolution (LR) text images, thereby providing high-quality visual inputs for scene text recognition, intelligent document processing, and visual retrieval in natural environments. Owing to factors such as imaging distance, focal variation, motion blur, and complex background interference, LR scene text images often contain broken strokes, blurred character boundaries, distorted contours, and missing structural details, reducing the accuracy and robustness of downstream text recognition models. Existing STISR methods mainly focus on visual texture reconstruction or text-related semantic guidance and remain inadequate for recovering character contours and fine-grained structures under severe degradation. Although diffusion models show strong detail generation capability, without explicit structural constraints, the denoising process may introduce local artifacts, structural shifts, or incomplete stroke recovery when processing highly degraded text images. To address these problems, a Latent Structure Prior-Prompted Diffusion Model (LSPDiff) was constructed for scene text image super-resolution. LSPDiff comprised a Latent Structure Generation Model (LSGM) and a Prior-Prompted Diffusion Model (PPDM). The LSGM took bicubic-interpolated low-resolution text images as input, learned multi-scale latent structural features through an encoder-decoder architecture, and used multiscale supervision to enhance character strokes, edge contours, local textures, and spatial arrangements at different resolutions. Multi-scale supervision is introduced to encourage the generated structural representations to approach the corresponding high-resolution text images. During reverse denoising, the PPDM jointly incorporated the low-resolution conditional image, noisy image, and structural prompts, and a prior-prompt attention module was designed to model the interaction between diffusion intermediate features and latent structural priors, enabling structural information to guide noise estimation and character reconstruction. Since the two models had different optimization objectives, LSGM focuses on structural prior learning while the PPDM focuses on diffusion-based noise prediction, their optimization objectives are not fully consistent. a two-stage training strategy was therefore adopted. The LSGM was first trained independently to obtain stable structural priors, after which its parameters were frozen, and the PPDM was optimized with fixed structural guidance to improve convergence stability and prior transfer effectiveness. Experiments were conducted on the TextZoom benchmark dataset, which contains 21 740 low-resolution and high-resolution scene text image pairs, including 17 367 pairs for training and 4 373 pairs for testing. The test set is divided into easy, medium, and hard subsets according to image degradation difficulty, allowing a detailed evaluation under different restoration conditions. Three scene text recognition models, namely ASTER, MORAN, and CRNN, were used to evaluate recognition accuracy on the restored images. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were also adopted to measure reconstruction quality. LSPDiff achieved average recognition accuracies of 66.2%, 63.3%, and 57.0% under ASTER, MORAN, and CRNN, respectively, as well as a PSNR of 22.32 dB and an SSIM of 0.792 2, demonstrating favorable performance in both recognition-oriented restoration and image reconstruction quality. Compared with representative STISR methods, LSPDiff obtains competitive overall results, especially in recognition accuracy and PSNR. Ablation experiments further verify the role of latent structural guidance. Compared with the PPDM-only variant, showed that introducing the LSGM increased PSNR from 21.40 dB to 22.32 dB and recognition accuracy from 60.5% to 66.2%. These results indicate that multi-scale latent structural priors provide effective guidance for recovering character strokes and maintaining text layout consistency. Multi-scale latent structural priors can effectively constrain the diffusion-based reconstruction process and improve stroke continuity, contour clarity, and text readability in low-resolution scene text images, and provide an effective approach to STISR under complex degradation conditions and has potential application value in scene text recognition, intelligent document processing, and visual information understanding.

Key words: scene text image super-resolution, latent structure prior, diffusion model, prior prompt, text recognition