Scene text recognition has been widely researched with supervised approaches. Most existing algorithms require a large amount of labeled data and some methods even require character-level or pixel-wise supervision information. However, labeled data is expensive, unlabeled data is relatively easy to collect, especially for many languages with fewer resources. In this paper, we propose a novel semi-supervised method for scene text recognition. Specifically, we design two global metrics, i.e., edit reward and embedding reward, to evaluate the quality of generated string and adopt reinforcement learning techniques to directly optimize these rewards. The edit reward measures the distance between the ground truth label and the generated string. Besides, the image feature and string feature are embedded into a common space and the embedding reward is defined by the similarity between the input image and generated string. It is natural that the generated string should be the nearest with the image it is generated from. Therefore, the embedding reward can be obtained without any ground truth information. In this way, we can effectively exploit a large number of unlabeled images to improve the recognition performance without any additional laborious annotations. Extensive experimental evaluations on the five challenging benchmarks, the Street View Text, IIIT5K, and ICDAR datasets demonstrate the effectiveness of the proposed approach, and our method significantly reduces annotation effort while maintaining competitive recognition performance.

Scene text recognition has attracted rapidly increasing attention from the research community. Recent dominant approaches typically follow an attention-based encoder-decoder framework that uses a unidirectional decoder to perform decoding in a left-to-right manner, but ignoring equally important right-to-left grammar information. In this paper, we propose a novel Gate-based Bidirectional Interactive Decoding Network (GBIDN) for scene text recognition. Firstly, the backward decoder performs decoding from right to left and generates the reverse language context. After that, the forward decoder simultaneously utilizes the visual context from image encoder and the reverse language context from backward decoder through two attention modules. In this way, the bidirectional decoders perform effective interaction to fully fuse the bidirectional grammar information and further improve the decoding quality. Besides, in order to relieve the adverse effect of noises, we devise a gated context mechanism to adaptively make use of the visual context and reverse language context. Extensive experiments on various challenging benchmarks demonstrate the effectiveness of our method.

Images can be considered as the combination of two parts: the content and the style. The authors’ approach can leverage this property by extracting a certain unique style from the reference images and combining it to generate images with new contents. With a well‐defined style feature extraction module, they propose a novel framework to generate images with various styles and the same content. To train the style specific image generation model efficiently, a double‐cycle training strategy is proposed: they input two natural‐content pairs simultaneously, extract their style features, and exchange them twice to obtain the reconstruction of the input natural images. What is more, they apply the triplet margin loss to the style feature extracted from the images before and after style exchange and an adversarial discriminator to force the style‐exchanged images to be real. They perform experiments on licence‐plate image, Chinese characters, and shoes or handbags images generating, obtain photo‐realistic results and remarkably improve the corresponding supervised recognition task.

Reading text in the wild is a challenging task in computer vision. Scene text suffers from various background noise, including shadow, irrelevant symbols and background texture. In order to reduce the disturbance of background noise, we propose a dense chained attention network with stacked attention modules for scene text recognition. Each attention module learns the attention map that is adapted to corresponding features to enhance the foreground text and suppress the background noise. Besides, the attention branch is designed with the convolution-deconvolution structure which rapidly captures global information to guide the discriminative feature selection. We stack multiple attention modules to gradually refine the attention maps and capture both the low-level appearance feature and the high-level semantic information. Extensive experiments on the standard benchmarks, the Street View Text, IIIT5K, and ICDAR datasets validate the superiority of the proposed method. The dense chained attention network achieves state-of-the-art or highly competitive recognition performance.