A CNN Based Scene Chinese Text Recognition Algorithm With Synthetic Data Engine
This work addresses a domain-specific problem for computer vision applications involving Chinese text recognition, but it is incremental as it adapts existing English text recognition methods to Chinese with synthetic data.
The authors tackled the problem of limited training data for Chinese scene text recognition by developing a synthetic data engine to generate character images based on font frequency, and their CNN-based algorithm achieved better recognition accuracy compared to baseline methods on two Chinese text datasets.
Scene text recognition plays an important role in many computer vision applications. The small size of available public available scene text datasets is the main challenge when training a text recognition CNN model. In this paper, we propose a CNN based Chinese text recognition algorithm. To enlarge the dataset for training the CNN model, we design a synthetic data engine for Chinese scene character generation, which generates representative character images according to the fonts use frequency of Chinese texts. As the Chinese text is more complex, the English text recognition CNN architecture is modified for Chinese text. To ensure the small size nature character dataset and the large size artificial character dataset are comparable in training, the CNN model are trained progressively. The proposed Chinese text recognition algorithm is evaluated with two Chinese text datasets. The algorithm achieves better recognize accuracy compared to the baseline methods.