Chinese character recognition has attracted much interest due to its high challenge and various applications. The whole-character modeling method can recognize common characters well but unable to handle unseen situation. Some radical-based modeling methods have successfully achieved great performance in unseen condition but the decoding takes huge time comsumption. Therefore, a high-efficient model which can recognize unseen characters needs to be proposed. First, this paper introduces a novel radical counter network (RCN) to recognize Chinese characters by identifying radicals and spatial structures. The proposed RCN first extracts visual features from input by employing DenseNet as encoder. Then a decoder based on fully connected layer is employed, aiming at synchronously estimating the number of each caption in character. The manner of simultaneously decoding all the captions greatly saves time of sequence decoding. Additionally, we design a multi-task learning to combine global feature extraction capability of whole-character modeling and local feature extraction capability of radical-based modeling, which further improves the model generalization. Experiments on natural scene character dataset demonstrate that the proposed model significantly outperforms baseline by 4.81\% with a comparable model complexity. That shows great robustness and simplicity of our model.