For any digital application with document images such as retrieval, the classification of document images becomes an essential stage. Conventionally for the purpose, the full versions of the documents, that is the uncompressed document images make the input dataset, which poses a threat due to the big volume required to accommodate the full versions of the documents. Therefore, it would be novel, if the same classification task could be accomplished directly (with some partial decompression) with the compressed representation of documents in order to make the whole process computationally more efficient. In this research work, a novel deep learning model—DWT-CompCNN—is proposed for classification of documents that are compressed using High Throughput JPEG 2000 (HTJ2K) algorithm. The proposed DWT-CompCNN comprises of five convolutional layers with filter sizes of 16, 32, 64, 128, and 256 consecutively for each increasing layer to improve learning from the wavelet coefficients extracted from the compressed images. Experiments are performed on two benchmark datasets, Tobacco-3482 and RVL-CDIP, which demonstrate that the proposed model is time and space efficient, and also achieves a better classification accuracy in compressed domain.

对于任何具有文档图像的数字应用（例如检索），文档图像的分类成为一个重要的阶段。通常，出于此目的，文档的完整版本，即未压缩的文档图像构成输入数据集，由于容纳文档的完整版本所需的大容量，这构成了威胁。因此，如果可以使用文档的压缩表示直接完成相同的分类任务（通过一些部分解压缩）以使整个过程在计算上更加高效，这将是新颖的。在这项研究工作中，提出了一种新颖的深度学习模型——DWT-CompCNN，用于对使用高吞吐量 JPEG 2000 (HTJ2K) 算法压缩的文档进行分类。所提出的 DWT-CompCNN 由五个卷积层组成，每个递增层的滤波器大小连续为 16、32、64、128 和 256，以改进从压缩图像中提取的小波系数的学习。在两个基准数据集Tobacco-3482和RVL-CDIP上进行了实验，结果表明所提出的模型具有时间和空间效率，并且在压缩域中实现了更好的分类精度。