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For any digital application with document images such as retrieval, theclassification of document images becomes an essential stage. Conventionallyfor the purpose, the full versions of the documents, that is the uncompresseddocument images make the input dataset, which poses a threat due to the bigvolume required to accommodate the full versions of the documents. Therefore,it would be novel, if the same classification task could be accomplisheddirectly (with some partial decompression) with the compressed representationof documents in order to make the whole process computationally more efficient.In this research work, a novel deep learning model, DWT CompCNN is proposed forclassification of documents that are compressed using High Throughput JPEG 2000(HTJ2K) algorithm. The proposed DWT-CompCNN comprises of five convolutionallayers with filter sizes of 16, 32, 64, 128, and 256 consecutively for eachincreasing layer to improve learning from the wavelet coefficients extractedfrom the compressed images. Experiments are performed on two benchmarkdatasets- Tobacco-3482 and RVL-CDIP, which demonstrate that the proposed modelis time and space efficient, and also achieves a better classification accuracyin compressed domain.

DWT-CompCNN: Deep Image Classification Network for High Throughput JPEG 2000 Compressed Documents