Few-shot Shadow Removal Method for Text Recognition

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.