Hybrid CNN-ViT Framework for Motion-Blurred Scene Text Restoration

TL;DR

This paper presents a hybrid CNN-ViT deep learning framework that effectively restores motion-blurred scene text images, improving readability and performance in computer vision tasks with high accuracy and efficiency.

Contribution

It introduces a novel CNN-ViT hybrid architecture specifically designed for motion-blurred scene text restoration, combining local and global feature modeling.

Findings

Achieves 32.20 dB PSNR and 0.934 SSIM on benchmark datasets.

Maintains lightweight design with 2.83 million parameters.

Operates with an average inference time of 61 ms.

Abstract

Motion blur in scene text images severely impairs readability and hinders the reliability of computer vision tasks, including autonomous driving, document digitization, and visual information retrieval. Conventional deblurring approaches are often inadequate in handling spatially varying blur and typically fall short in modeling the long-range dependencies necessary for restoring textual clarity. To overcome these limitations, we introduce a hybrid deep learning framework that combines convolutional neural networks (CNNs) with vision transformers (ViTs), thereby leveraging both local feature extraction and global contextual reasoning. The architecture employs a CNN-based encoder-decoder to preserve structural details, while a transformer module enhances global awareness through self-attention. Training is conducted on a curated dataset derived from TextOCR, where sharp scene-text samples are paired with synthetically blurred versions generated using realistic motion-blur kernels of multiple sizes and orientations. Model optimization is guided by a composite loss that incorporates mean absolute error (MAE), squared error (MSE), perceptual similarity, and structural similarity (SSIM). Quantitative evaluations show that the proposed method attains 32.20 dB in PSNR and 0.934 in SSIM, while remaining lightweight with 2.83 million parameters and an average inference time of 61 ms. These results highlight the effectiveness and computational efficiency of the CNN-ViT hybrid design, establishing its practicality for real-world motion-blurred scene-text restoration.