🤖 AI Summary
Existing text removal methods primarily target simple outdoor scenes and struggle with real-world images containing high-density, complex text layouts; moreover, their performance is highly sensitive to mask shape, necessitating costly manual parameter tuning. To address dense text images, this paper proposes an automated mask shape learning framework integrating deformable mask modeling and Bayesian optimization. First, we construct character-level deformable contour masks and empirically demonstrate that minimal covering masks are suboptimal, highlighting the critical role of fine-grained contour adjustment. Second, we formulate mask shape optimization as a black-box problem, using restoration quality as the feedback objective and leveraging Bayesian optimization to automatically determine optimal shape parameters. Experiments show significant improvements in restoration quality on high-density text images, empirically validating the existence of an optimal mask shape. Our approach delivers an interpretable, reusable, and fully automated solution for industrial-grade text removal.
📝 Abstract
The advent of generative models has dramatically improved the accuracy of image inpainting. In particular, by removing specific text from document images, reconstructing original images is extremely important for industrial applications. However, most existing methods of text removal focus on deleting simple scene text which appears in images captured by a camera in an outdoor environment. There is little research dedicated to complex and practical images with dense text. Therefore, we created benchmark data for text removal from images including a large amount of text. From the data, we found that text-removal performance becomes vulnerable against mask profile perturbation. Thus, for practical text-removal tasks, precise tuning of the mask shape is essential. This study developed a method to model highly flexible mask profiles and learn their parameters using Bayesian optimization. The resulting profiles were found to be character-wise masks. It was also found that the minimum cover of a text region is not optimal. Our research is expected to pave the way for a user-friendly guideline for manual masking.