Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes
This addresses the problem of data scarcity for researchers and practitioners in computer vision, specifically for scene text analysis, though it is an incremental improvement in synthesis methods.
The paper tackles the challenge of needing large annotated datasets for training deep neural networks in scene text detection and recognition by introducing a novel image synthesis technique that generates annotated scene text images. The result is superior performance in training accurate and robust models, as demonstrated through evaluations on five public datasets.
The requirement of large amounts of annotated images has become one grand challenge while training deep neural network models for various visual detection and recognition tasks. This paper presents a novel image synthesis technique that aims to generate a large amount of annotated scene text images for training accurate and robust scene text detection and recognition models. The proposed technique consists of three innovative designs. First, it realizes "semantic coherent" synthesis by embedding texts at semantically sensible regions within the background image, where the semantic coherence is achieved by leveraging the semantic annotations of objects and image regions that have been created in the prior semantic segmentation research. Second, it exploits visual saliency to determine the embedding locations within each semantic sensible region, which coincides with the fact that texts are often placed around homogeneous regions for better visibility in scenes. Third, it designs an adaptive text appearance model that determines the color and brightness of embedded texts by learning from the feature of real scene text images adaptively. The proposed technique has been evaluated over five public datasets and the experiments show its superior performance in training accurate and robust scene text detection and recognition models.