Ground truth data for the Optical Character Recognition of Historical Classical Commentaries.

Related tags

Deep LearningGT-commentaries-OCR
Overview

OCR Ground Truth for Historical Commentaries

DOI License: CC BY 4.0

The dataset OCR ground truth for historical commentaries (GT4HistComment) was created from the public domain subset of scholarly commentaries on Sophocles' Ajax. Its main goal is to enable the evaluation of the OCR quality on printed materials that contain a mix of Latin and polytonic Greek scripts. It consists of five 19C commentaries written in German, English, and Latin, for a total of 3,356 GT lines.

Data

GT4HistComment are contained in data/, where each sub-folder corresponds to a different publication (i.e. commentary). For each each commentary we provide the following data:

  • <commentary_id>/GT-pairs: pairs of image/text files for each GT line
  • <commentary_id>/imgs: original images on which the OCR was performed
  • <commentary_id>/<commentary_id>_olr.tsv: OLR annotations with image region coordinates and layout type ground truth label

The OCR output produced by the Kraken + Ciaconna pipeline was manually corrected by a pool of annotators using the Lace platform. In order to ensure the quality of the ground truth datasets, an additional verification of all transcriptions made in Lace was carried out by an annotator on line-by-line pairs of image and corresponding text.

Commentary overview

ID Commentator Year Languages Image source Line example
bsb10234118 Lobeck [1] 1835 Greek, Latin BSB
sophokle1v3soph Schneidewin [2] 1853 Greek, German Internet Archive
cu31924087948174 Campbell [3] 1881 Greek, English Internet Archive
sophoclesplaysa05campgoog Jebb [4] 1896 Greek, English Internet Archive
Wecklein1894 Wecklein [5] 1894 [5] Greek. German internal

Stats

Line, word and char counts for each commentary are indicated in the following table. Detailled counts for each region can be found here.

ID Commentator Type lines words all chars greek chars
bsb10234118 Lobeck training 574 2943 16081 5344
bsb10234118 Lobeck groundtruth 202 1491 7917 2786
sophokle1v3soph Schneidewin training 583 2970 16112 3269
sophokle1v3soph Schneidewin groundtruth 382 1599 8436 2191
cu31924087948174 Campbell groundtruth 464 2987 14291 3566
sophoclesplaysa05campgoog Jebb training 561 4102 19141 5314
sophoclesplaysa05campgoog Jebb groundtruth 324 2418 10986 2805
Wecklein1894 Wecklein groundtruth 211 1912 9556 3268

Commentary editions used:

  • [1] Lobeck, Christian August. 1835. Sophoclis Aiax. Leipzig: Weidmann.
  • [2] Sophokles. 1853. Sophokles Erklaert von F. W. Schneidewin. Erstes Baendchen: Aias. Philoktetes. Edited by Friedrich Wilhelm Schneidewin. Leipzig: Weidmann.
  • [3] Lewis Campbell. 1881. Sophocles. Oxford : Clarendon Press.
  • [4] Wecklein, Nikolaus. 1894. Sophokleus Aias. München: Lindauer.
  • [5] Jebb, Richard Claverhouse. 1896. Sophocles: The Plays and Fragments. London: Cambridge University Press.

Citation

If you use this dataset in your research, please cite the following publication:

@inproceedings{romanello_optical_2021,
  title = {Optical {{Character Recognition}} of 19th {{Century Classical Commentaries}}: The {{Current State}} of {{Affairs}}},
  booktitle = {The 6th {{International Workshop}} on {{Historical Document Imaging}} and {{Processing}} ({{HIP}} '21)},
  author = {Romanello, Matteo and Sven, Najem-Meyer and Robertson, Bruce},
  year = {2021},
  publisher = {{Association for Computing Machinery}},
  address = {{Lausanne}},
  doi = {10.1145/3476887.3476911}
}

Acknowledgements

Data in this repository were produced in the context of the Ajax Multi-Commentary project, funded by the Swiss National Science Foundation under an Ambizione grant PZ00P1_186033.

Contributors: Carla Amaya (UNIL), Sven Najem-Meyer (EPFL), Matteo Romanello (UNIL), Bruce Robertson (Mount Allison University).

You might also like...
Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

Visual 3D Detection Package: This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from

Yuxuan Liu 305 Dec 19, 2022
[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints Official implementation for Reducing Footskate in Human Motion Recon

Virginia Tech Vision and Learning Lab 38 Nov 1, 2022
PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb
PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

halo 368 Dec 6, 2022
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

GndNet: Fast Ground plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Authors: Anshul Paigwar, Ozgur Erkent, David Sierra Gonzale

Anshul Paigwar 114 Dec 29, 2022
Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python

Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python THIS PROJECT IS CURRENTLY A WORK IN PROGRESS AND THUS THIS REPOSITORY I

Joshua Marshall 14 Dec 31, 2022
Using LSTM to detect spoofing attacks in an Air-Ground network
Using LSTM to detect spoofing attacks in an Air-Ground network

Using LSTM to detect spoofing attacks in an Air-Ground network Specifications IDE: Spider Packages: Tensorflow 2.1.0 Keras NumPy Scikit-learn Matplotl

Tiep M. H. 1 Nov 20, 2021
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

null 77 Jan 5, 2023
Implementation of
Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

PyGAS: Auto-Scaling GNNs in PyG PyGAS is the practical realization of our G NN A uto S cale (GAS) framework, which scales arbitrary message-passing GN

Matthias Fey 139 Dec 25, 2022
A two-stage U-Net for high-fidelity denoising of historical recordings
A two-stage U-Net for high-fidelity denoising of historical recordings

A two-stage U-Net for high-fidelity denoising of historical recordings Official repository of the paper (not submitted yet): E. Moliner and V. Välimäk

Eloi Moliner Juanpere 57 Jan 5, 2023
Comments
  • adds line-, word- and char-counts to README.md

    adds line-, word- and char-counts to README.md

    Adds a table to README.md as suggested by reviewer 1. The table also link to a more complete table, itself a public version of spreadsheet OCR evaluation and stats!detailed_counts. Note that the publishable version is an external reference to our private version, meaning that actualising the latter will also update the former.

    opened by sven-nm 0
  • Pages à exclure - OCR

    Pages à exclure - OCR

    La page contient les schémas métriques des passages. De ce fait l'OCR ne les reconnaît pas, de plus la correction de l'OCR n'a pas été achevée.

    Voici les pages à exclure : sophoclesplaysa05campgoog_0072.png (Jebb, p. 72)

    opened by camaya28 0
Releases(v1.0)
Owner
Ajax Multi-Commentary
How does a classical hero die in the digital age? Using Sophocles’ Ajax to create a commentary on commentaries.
Ajax Multi-Commentary
GitHub Repository
An Straight Dilated Network with Wavelet for image Deblurring

SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring(offical) 1. Introduction This repo is not only used for our paper(

FlyEgle 41 Jan 04, 2023
Unofficial PyTorch Implementation for HifiFace (https://arxiv.org/abs/2106.09965)

HifiFace — Unofficial Pytorch Implementation Image source: HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping (figure 1, pg. 1)

MINDs Lab 218 Jan 04, 2023
MAVE: : A Product Dataset for Multi-source Attribute Value Extraction

MAVE: : A Product Dataset for Multi-source Attribute Value Extraction The dataset contains 3 million attribute-value annotations across 1257 unique ca

Google Research Datasets 89 Jan 08, 2023
[ICCV' 21] "Unsupervised Point Cloud Pre-training via Occlusion Completion"

OcCo: Unsupervised Point Cloud Pre-training via Occlusion Completion This repository is the official implementation of paper: "Unsupervised Point Clou

Hanchen 204 Dec 24, 2022
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

Yuan Yin 24 Oct 24, 2022
Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering (NAACL 2021)

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering Abstract In open-domain question answering (QA), retrieve-and-read mec

Clova AI Research 34 Apr 13, 2022
Weakly Supervised Segmentation by Tensorflow.

Weakly Supervised Segmentation by Tensorflow. Implements semantic segmentation in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

CHENG-YOU LU 52 Dec 27, 2022
Transfer SemanticKITTI labeles into other dataset/sensor formats.

LiDAR-Transfer Transfer SemanticKITTI labeles into other dataset/sensor formats. Content Convert datasets (NUSCENES, FORD, NCLT) to KITTI format Minim

Photogrammetry & Robotics Bonn 64 Nov 21, 2022
Accelerated Multi-Modal MR Imaging with Transformers

Accelerated Multi-Modal MR Imaging with Transformers Dependencies numpy==1.18.5 scikit_image==0.16.2 torchvision==0.8.1 torch==1.7.0 runstats==1.8.0 p

54 Dec 16, 2022
A 1.3B text-to-image generation model trained on 14 million image-text pairs

minDALL-E on Conceptual Captions minDALL-E, named after minGPT, is a 1.3B text-to-image generation model trained on 14 million image-text pairs for no

Kakao Brain 604 Dec 14, 2022
Some useful blender add-ons for SMPL skeleton's poses and global translation.

Blender add-ons for SMPL skeleton's poses and trans There are two blender add-ons for SMPL skeleton's poses and trans.The first is for making an offli

犹在镜中 154 Jan 04, 2023
Madanalysis5 - A package for event file analysis and recasting of LHC results

Welcome to MadAnalysis 5 Outline What is MadAnalysis 5? Requirements Downloading

MadAnalysis 15 Jan 01, 2023
Parameter Efficient Deep Probabilistic Forecasting

PEDPF Parameter Efficient Deep Probabilistic Forecasting (PEDPF) is a repository containing code to run experiments for several deep learning based pr

Olivier Sprangers 10 Jun 13, 2022
Open source implementation of AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing

AceNAS This repo is the experiment code of AceNAS, and is not considered as an official release. We are working on integrating AceNAS as a built-in st

Yuge Zhang 6 Sep 07, 2022
We utilize deep reinforcement learning to obtain favorable trajectories for visual-inertial system calibration.

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning Update: The lastest code will be updated in this branch. Pleas

ETHZ ASL 27 Dec 29, 2022
PlaidML is a framework for making deep learning work everywhere.

A platform for making deep learning work everywhere. Documentation | Installation Instructions | Building PlaidML | Contributing | Troubleshooting | R

PlaidML 4.5k Jan 02, 2023
The official implementation of paper "Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks" (IJCV under review).

DGMS This is the code of the paper "Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks". Installation Our code works with Pytho

Runpei Dong 3 Aug 28, 2022
Deeplearning project at The Technological University of Denmark (DTU) about Neural ODEs for finding dynamics in ordinary differential equations and real world time series data

Authors Marcus Lenler Garsdal, [email protected] Valdemar Søgaard, Simon Moe Sørensen 6 Sep 05, 2022

CTF challenges from redpwnCTF 2021

redpwnCTF 2021 Challenges This repository contains challenges from redpwnCTF 2021 in the rCDS format; challenge information is in the challenge.yaml f

redpwn 27 Dec 07, 2022
[ICML 2021] Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data

Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data This repo provides the source code & data of our paper: Break-It-Fix-It: Unsupervised

Michihiro Yasunaga 86 Nov 30, 2022