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），文档图像分类成为关键环节。传统上，为完成该任务，需使用文档的完整版本（即未压缩的文档图像）作为输入数据集，但存储完整版本所需的大容量带来了挑战。因此，若能直接基于文档的压缩表示（经部分解压缩）完成相同分类任务，将具有创新性，并可提升整个流程的计算效率。本研究提出了一种新型深度学习模型DWT-CompCNN，用于对采用高吞吐量JPEG 2000（HTJ2K）算法压缩的文档进行分类。该模型包含五个卷积层，各层滤波器大小依次为16、32、64、128和256，通过逐层扩展以增强从压缩图像提取的小波系数中学习特征的能力。在两个基准数据集（Tobacco-3482和RVL-CDIP）上的实验表明，所提模型在时间和空间上均具有高效性，且在压缩域中实现了更优的分类准确率。