# Entropy Computation of Document Images in Run-Length Compressed Domain

arXiv: https://arxiv.org/abs/1404.2014 · HTML: https://mlsanity.com/paper/1404.2014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2014.

## Built on by (most cited first, 2 of 2)

- [DWT-CompCNN: Deep Image Classification Network for High Throughput JPEG 2000 Compressed Documents](https://mlsanity.com/paper/2306.01359/) (2023)
- [OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model](https://mlsanity.com/paper/2209.09118/) (2022)

## Abstract (arXiv)

Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer. However, in order to process or carry out any analytical computations, decompression has become an unavoidable pre-requisite. In this research work, we have attempted to compute the entropy, which is an important document analytic directly from the compressed documents. We use Conventional Entropy Quantifier (CEQ) and Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies obtained are useful in applications like establishing equivalence, word spotting and document retrieval. Experiments have been performed with all the data sets of [1], at character, word and line levels taking compressed documents in run-length compressed domain. The algorithms developed are computational and space efficient, and results obtained match 100% with the results reported in [1].