  Most existing text reading benchmarks make it difficult to evaluate the
performance of more advanced deep learning models in large vocabularies due to
the limited amount of training data. To address this issue, we introduce a new
large-scale text reading benchmark dataset named Chinese Street View Text
(C-SVT) with 430,000 street view images, which is at least 14 times as large as
the existing Chinese text reading benchmarks. To recognize Chinese text in the
wild while keeping large-scale datasets labeling cost-effective, we propose to
annotate one part of the CSVT dataset (30,000 images) in locations and text
labels as full annotations and add 400,000 more images, where only the
corresponding text-of-interest in the regions is given as weak annotations. To
exploit the rich information from the weakly annotated data, we design a text
reading network in a partially supervised learning framework, which enables to
localize and recognize text, learn from fully and weakly annotated data
simultaneously. To localize the best matched text proposals from weakly labeled
images, we propose an online proposal matching module incorporated in the whole
model, spotting the keyword regions by sharing parameters for end-to-end
training. Compared with fully supervised training algorithms, this model can
improve the end-to-end recognition performance remarkably by 4.03% in F-score
at the same labeling cost. The proposed model can also achieve state-of-the-art
results on the ICDAR 2017-RCTW dataset, which demonstrates the effectiveness of
the proposed partially supervised learning framework.
