基于改进卷积神经网络的多源数字识别算法

现有的数字识别算法多是对单一类型数字进行识别，无法应对识别多源数字。针对包含手写体数字与数码管数字的字符识别场景，提出一种基于改进卷积神经网络（CNN）的多源数字识别算法。首先，使用从数显仪表生产企业现场采集的样本，结合MINIST数据集，建立起包含手写体和数码管的混合数据集；然后，考虑更好的鲁棒性，提出一种改进的CNN，并用上述混合数据集对其训练，实现了一个网络识别多类型数字；最后，训练好的神经网络模型被成功应用于RoboMaster机甲大赛的多源数字识别场景中。测试结果表明，所提算法整体识别准确率稳定且较高，具有较好的鲁棒性和泛化能力。

Multi-source digit recognition algorithm based on improved convolutional neural network

Most of the existing digit recognition algorithms recognize single-type digits, and can not recognize multi-source digits. Aiming at the character recognition scenarios with handwritten digits and digital tube digits, a multi-source digit recognition algorithm based on improved Convolutional Neural Network (CNN) was proposed. Firstly, a mixed data set consisting of handwritten and digital tube digits was established by using the samples collected from the field of digital display instrument manufacturer and MINIST data set. Then, considering better robustness, an improved CNN was proposed, which was trained by the above mixed data set, and a network was realized to recognize multi-type digits. Finally, the trained neural network model was successfully applied to the multi-source digit recognition scene of RoboMaster robotics competition. The test results show that, the overall recognition accuracy of the proposed algorithm is stable and high, and it has good robustness and generalization ability.