| 摘要(英) |
In recent years, scene text analysis based on deep learning techniques draw
a lot of research attention. Text detection in natural scenes is an important step of
scene text analysis and most of the existing text detection designs are based on
string detection. However, a string may contain words of different languages so it
is not easy to mark the language to which the string belongs accurately. Scene text
recognition using string-level annotations need to consider the effect of irregular
orientations and require a lot of training data and training time. Conversely,
character-based recognition methodologies do not need to consider orientations,
which simplifies the training processes. Multilingual natural scene text
recognition may be benefited from the flexibility of selecting suitable recognition
models according to different language characteristics. In this research, we use a
high-resolution network architecture to label word regions and point out the
centers of characters, and also employ multiple channels for substring language
classification. Due to the lack of character-level annotations in real datasets, we
propose a weakly supervised learning approach for characters, enabling the
network to improve the detection of characters significantly. The performance of
multi-language recognition is verified by using individual classifiers after
detection or by performing language recognition at the same time. The feasibility
of the proposed design is verified by showing the character detection of different
languages, including Latin, Chinese, Japanese, and Korean, as examples. |
| 參考文獻 |
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