面向文本识别的小样本阴影消除方法

Few-shot Shadow Removal Method for Text Recognition

阴影消除是计算机视觉领域中面对阴影场景的重要任务,旨在检测和消除图像中的阴影区域。由于图像编辑技术受到阴影图像质量的制约,现有方法利用其他任务中的知识和阴影特性来获得更加有效的特征向量,从而实现阴影消除。在带有文本内容的阴影图像中,由于文本颜色和形状等特征不同于前景和背景,因此可能将文本错误地检测为阴影的一部分进而导致错误的阴影消除结果。针对该问题,提出了一种面向文本识别的小样本阴影消除方法。在小样本目标检测基础框架模型中,利用被错误识别为阴影的文本特征生成基类数据和新类数据,增强对该类文本的特征学习;在部分检测框合并算法中,利用文本本身长宽比多样化、变化大的特性,以多个约束为前提合并结构相关性较强的检测框,实现对目标的正确框定。建立在真实数据与合成数据上的实验结果验证了所提方法的有效性。

Shadow removal is an important task in the field of computer vision,with the goal of detecting and removing shaded regions from shadow regions in images.As image editing techniques are constrained by the quality of shaded images,existing me-thods exploit the knowledge from other tasks and the properties of shadows to obtain more effective feature vectors for shadow removal.Since the color and shape features of the text differ from the foreground and background in the shaded images,the text may be incorrectly detected as part of the shadows to generate incorrect results.To address this problem,a few-shot shadow removal method for text recognition is proposed.First,the features of the text incorrectly identified as shadows are used to produce base class data and new class data to enhance feature learning of such text in the infrastructure part of the few-shot target detection model.Second,the text itself is used to merge structurally relevant detection frames with multiple constraints to fix the objects correctly in the enhancement part of the detection frame merging algorithm.Experimental results validate the effectiveness of the proposed method on real and synthetic datasets.