# Digital Peter: Dataset, Competition and Handwriting Recognition Methods

arXiv: https://arxiv.org/abs/2103.09354 · HTML: https://mlsanity.com/paper/2103.09354/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [Robust Scene Text Recognition with Automatic Rectification](https://mlsanity.com/paper/1603.03915/) (2016)
- [Recursive Recurrent Nets with Attention Modeling for OCR in the Wild](https://mlsanity.com/paper/1603.03101/) (2016)
- [Rosetta: Large scale system for text detection and recognition in images](https://mlsanity.com/paper/1910.05085/) (2019)
- [A Two-Stage Method for Text Line Detection in Historical Documents](https://mlsanity.com/paper/1802.03345/) (2018)
- [A Large Dataset of Historical Japanese Documents with Complex Layouts](https://mlsanity.com/paper/2004.08686/) (2020)
- [READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents](https://mlsanity.com/paper/1705.03311/) (2017)
- [Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts](https://mlsanity.com/paper/1912.07025/) (2019)
- [CITlab ARGUS for Arabic Handwriting](https://mlsanity.com/paper/1412.6061/) (2014)

## Built on by (most cited first, 5 of 5)

- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts](https://mlsanity.com/paper/2401.08425/) (2024)
- [RusTitW: Russian Language Text Dataset for Visual Text in-the-Wild Recognition](https://mlsanity.com/paper/2303.16531/) (2023)
- [Handwritten text generation and strikethrough characters augmentation](https://mlsanity.com/paper/2112.07395/) (2021)
- [SIMARA: a database for key-value information extraction from full pages](https://mlsanity.com/paper/2304.13606/) (2023)

## Abstract (arXiv)

This paper presents a new dataset of Peter the Great's manuscripts and describes a segmentation procedure that converts initial images of documents into the lines. The new dataset may be useful for researchers to train handwriting text recognition models as a benchmark for comparing different models. It consists of 9 694 images and text files corresponding to lines in historical documents. The open machine learning competition Digital Peter was held based on the considered dataset. The baseline solution for this competition as well as more advanced methods on handwritten text recognition are described in the article. Full dataset and all code are publicly available.