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

arXiv: https://arxiv.org/abs/2306.01359 · HTML: https://mlsanity.com/paper/2306.01359/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [Accelerating Very Deep Convolutional Networks for Classification and Detection](https://mlsanity.com/paper/1505.06798/) (2015)
- [Wavelet Integrated CNNs for Noise-Robust Image Classification](https://mlsanity.com/paper/2005.03337/) (2020)
- [Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification](https://mlsanity.com/paper/1704.03557/) (2017)
- [Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1801.09321/) (2018)
- [Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines](https://mlsanity.com/paper/1711.05862/) (2017)
- [Analysis of Convolutional Neural Networks for Document Image Classification](https://mlsanity.com/paper/1708.03273/) (2017)
- [DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients](https://mlsanity.com/paper/1907.11503/) (2019)
- [Improving accuracy and speeding up Document Image Classification through parallel systems](https://mlsanity.com/paper/2006.09141/) (2020)
- [What is the right way to represent document images?](https://mlsanity.com/paper/1603.01076/) (2016)
- [Remote Sensing Image Scene Classification with Deep Neural Networks in JPEG 2000 Compressed Domain](https://mlsanity.com/paper/2006.11529/) (2020)
- [Efficient Document Image Classification Using Region-Based Graph Neural Network](https://mlsanity.com/paper/2106.13802/) (2021)
- [Entropy Computation of Document Images in Run-Length Compressed Domain](https://mlsanity.com/paper/1404.2014/) (2014)
- [Text line Segmentation in Compressed Representation of Handwritten Document using Tunneling Algorithm](https://mlsanity.com/paper/1901.11477/) (2019)
- [Document image classification, with a specific view on applications of patent images](https://mlsanity.com/paper/1601.03295/) (2016)

## Built on by (most cited first, 1 of 1)

- [CompTLL-UNet: Compressed Domain Text-Line Localization in Challenging Handwritten Documents using Deep Feature Learning from JPEG Coefficients](https://mlsanity.com/paper/2308.06142/) (2023)

## Abstract (arXiv)

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.