  We introduce Chinese Text in the Wild, a very large dataset of Chinese text
in street view images. While optical character recognition (OCR) in document
images is well studied and many commercial tools are available, detection and
recognition of text in natural images is still a challenging problem,
especially for more complicated character sets such as Chinese text. Lack of
training data has always been a problem, especially for deep learning methods
which require massive training data.
  In this paper we provide details of a newly created dataset of Chinese text
with about 1 million Chinese characters annotated by experts in over 30
thousand street view images. This is a challenging dataset with good diversity.
It contains planar text, raised text, text in cities, text in rural areas, text
under poor illumination, distant text, partially occluded text, etc. For each
character in the dataset, the annotation includes its underlying character, its
bounding box, and 6 attributes. The attributes indicate whether it has complex
background, whether it is raised, whether it is handwritten or printed, etc.
The large size and diversity of this dataset make it suitable for training
robust neural networks for various tasks, particularly detection and
recognition. We give baseline results using several state-of-the-art networks,
including AlexNet, OverFeat, Google Inception and ResNet for character
recognition, and YOLOv2 for character detection in images. Overall Google
Inception has the best performance on recognition with 80.5% top-1 accuracy,
while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and
trained models will all be publicly available on the website.
