| dc.description.abstract | Texts in nature scenes, especially street views, usually contain rich information related to the images. Although recognition of scanned documents has been well studied, scene text recognition is still a challenging task due to variable text fonts, inconsistent lighting conditions, different text orientations, background noises, angle of camera shooting and possible image distortions. This research aims at developing effective Traditional Chinese recognition scheme for streetscape based on deep learning techniques. It should be noted that constructing a suitable training dataset is an essential step and will affect the recognition performance significantly. However, the large alphabet size of Chinese characters is certainly an issue, which may cause the so-called data imbalance problem when collecting corresponding images. In the proposed scheme, a synthetic dataset with automatic labeling is constructed using several fonts and data augmentation. In an investigated image, the potential regions of characters and text-lines are located. For the located single characters, the possibly skewed images are rectified by the spatial transform network to enhance the performance. Next, the proposed attention-residual network improves the recognition accuracy in this large-scale classification. Finally, the recognized characters are combined using detected text-lines and corrected by the information from Google Place API with the location information. The experimental results show that the proposed scheme can correctly extract the texts from the selected areas in investigated images. The recognition performance is superior to Line OCR and Google Vision in complex street scenes. | en_US |