# Brno Mobile OCR Dataset

arXiv: https://arxiv.org/abs/1907.01307 · HTML: https://mlsanity.com/paper/1907.01307/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [Page Layout Analysis System for Unconstrained Historic Documents](https://mlsanity.com/paper/2102.11838/) (2021)
- [Detection Masking for Improved OCR on Noisy Documents](https://mlsanity.com/paper/2205.08257/) (2022)
- [3D Rendering Framework for Data Augmentation in Optical Character Recognition](https://mlsanity.com/paper/2209.14970/) (2022)
- [Self-supervised Pre-training of Text Recognizers](https://mlsanity.com/paper/2405.00420/) (2024)
- [BusiNet -- a Light and Fast Text Detection Network for Business Documents](https://mlsanity.com/paper/2207.01220/) (2022)
- [Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents](https://mlsanity.com/paper/2311.15740/) (2023)
- [PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents](https://mlsanity.com/paper/2403.15724/) (2024)

## Abstract (arXiv)

We introduce the Brno Mobile OCR Dataset (B-MOD) for document Optical Character Recognition from low-quality images captured by handheld mobile devices. While OCR of high-quality scanned documents is a mature field where many commercial tools are available, and large datasets of text in the wild exist, no existing datasets can be used to develop and test document OCR methods robust to non-uniform lighting, image blur, strong noise, built-in denoising, sharpening, compression and other artifacts present in many photographs from mobile devices. This dataset contains 2 113 unique pages from random scientific papers, which were photographed by multiple people using 23 different mobile devices. The resulting 19 728 photographs of various visual quality are accompanied by precise positions and text annotations of 500k text lines. We further provide an evaluation methodology, including an evaluation server and a testset with non-public annotations. We provide a state-of-the-art text recognition baseline build on convolutional and recurrent neural networks trained with Connectionist Temporal Classification loss. This baseline achieves 2 %, 22 % and 73 % word error rates on easy, medium and hard parts of the dataset, respectively, confirming that the dataset is challenging. The presented dataset will enable future development and evaluation of document analysis for low-quality images. It is primarily intended for line-level text recognition, and can be further used for line localization, layout analysis, image restoration and text binarization.