  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.
