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.

翻译：对于任何涉及文档图像的数字应用（如检索），文档图像分类成为一个关键阶段。传统上，为实现该目的，文档的完整版本（即未压缩的文档图像）构成输入数据集，这因存储完整版本所需的大容量而带来隐患。因此，若能直接利用文档的压缩表示（通过部分解压缩）完成相同的分类任务，则具有创新性，可使整个过程在计算上更高效。本研究提出一种新型深度学习模型DWT-CompCNN，用于对采用高通量JPEG 2000（HTJ2K）算法压缩的文档进行分类。所提出的DWT-CompCNN包含五个卷积层，其滤波器尺寸依次为16、32、64、128和256，以提升从压缩图像提取的小波系数中学习的能力。在两个基准数据集Tobacco-3482和RVL-CDIP上进行的实验表明，该模型在时间和空间上具有高效性，且在压缩域中达到了更优的分类精度。