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.

本研究提出了一种名为DWT-CompCNN的基于深度学习的模型，使用高通量JPEG 2000（HTJ2K）算法压缩文档，并从中提取离散小波系数，从而在压缩域中高效准确的对文档进行分类。实验表明该模型在时间和空间上的效率更高，而且能够取得更好的分类效果。

DWT-CompCNN: 高吞吐量JPEG2000压缩文档的深度图像分类网络