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.

本文提出了一种基于卷积神经网络的中文文本识别算法，并设计了一种合成数据引擎，用于生成代表性的中文场景字符图像来扩大数据集。通过对中文文本识别 CNN 架构进行修改，本算法在两个中文文本数据集上得到了更好的识别精度为基准方法。

基于CNN的带有合成数据引擎的场景中文文本识别算法