基于深度CNN和极限学习机相结合的实时文档分类

提出一种文档图像实时分类训练和测试的方法。在实际应用中,数据训练的精确性和高效性在文档图像识别中起着关键的作用。现有的深度学习方法不能满足此要求,因为需要大量的时间用于训练和微调深层次的网络架构。针对此问题,提出一种基于计算机视觉的新方法:第一阶段训练深度网络,作为特征提取器;第二阶段用极限学习机(ELM)用于分类。该方法的性能优于目前最先进的基于深度学习的相关方法,在Tobacco-3482数据集上的最终准确率为83.45%。与之前基于卷积神经网络(CNN)的方法相比,相对误差降低了26%。ELM的训练时间仅为1.156秒,对2 482张图像的整体预测时间是3.083秒。因此,该文档分类方法适合于大规模实时应用。

REAL-TIME DOCUMENT CLASSIFICATION BASED ON DEEP CNN AND EXTREME LEARNING MACHINE

This paper presented a real-time training and testing method for document image classification. In practical applications, the accuracy and efficiency of data training play a key role in document image recognition. The existing deep learning methods cannot meet this requirement, because they need a lot of time to train and fine-tune the deep network architecture. To solve this problem, we proposed a new method based on computer vision. The method was divided into two steps: the depth network was trained as a feature extractor;we used the extreme learning machine(ELM) for classification. The performance of this method is superior to the advanced methods based on deep learning. The final accuracy of this method on Tobacco-3482 dataset is 83.45%. Compared with the method based on convolution neural network, the relative error is reduced by 26%. The training time of ELM is only 1.156 s, and the overall prediction time of 2 482 images is 3.083 s. Therefore, the method is suitable for large-scale real-time applications.