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Imagery texts are usually organized as a hierarchy of several visualelements, i.e. characters, words, text lines and text blocks. Among theseelements, character is the most basic one for various languages such asWestern, Chinese, Japanese, mathematical expression and etc. It is natural andconvenient to construct a common text detection engine based on characterdetectors. However, training character detectors requires a vast of locationannotated characters, which are expensive to obtain. Actually, the existingreal text datasets are mostly annotated in word or line level. To remedy thisdilemma, we propose a weakly supervised framework that can utilize wordannotations, either in tight quadrangles or the more loose bounding boxes, forcharacter detector training. When applied in scene text detection, we are thusable to train a robust character detector by exploiting word annotations in therich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. Thecharacter detector acts as a key role in the pipeline of our text detectionengine. It achieves the state-of-the-art performance on several challengingscene text detection benchmarks. We also demonstrate the flexibility of ourpipeline by various scenarios, including deformed text detection and mathexpression recognition.

WordSup: Exploiting Word Annotations for Character based Text Detection