基于 CNNs 识别反馈的点阵字符检测方法

针对点阵字符因错误分割导致识别准确率较低的问题,提出了一种基于卷积神经网络(CNNs)识别反馈的点阵字符检 测方法。 首先采用多尺度窗口获取多个候选区域并建立 CNNs 对其进行识别,利用投票机制对多个识别结果进行综合决策,然 后根据决策结果反向定位点阵字符并完成字符分割,最后提出一种滑动翻转窗口对所有字符进行分割与识别。 实验结果表明, 该方法在点阵字符的定位准确率和识别率方面都优于传统字符识别方法,分别达到了 97. 53%和 97. 50%。

Dot matrix character detection method based on CNNs recognition feedback

The recognition accuracy of dot matrix characters is low due to error segmentation, this paper proposes a dot matrix character detection method based on convolutional neural network (CNNs) recognition feedback. Firstly, multi-scale windows are used to acquire multiple candidate regions and CNNs are established to identify them. The voting mechanism is used to make comprehensive decisions on multiple recognition results, and then the lattice characters are reversed according to the decision result and the character segmentation is completed. Finally, a sliding flip window is proposed to segment and identify all characters. The experimental results show that the proposed method outperforms the traditional character recognition method in the segmentation accuracy and recognition rate of dot matrix characters, reaching 97. 53% and 97. 50% respectively.