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

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

[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

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

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

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

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

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.

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

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

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
🥇Samsung AI Challenge 2021 1등 솔루션입니다🥇

MoT - Molecular Transformer Large-scale Pretraining for Molecular Property Prediction Samsung AI Challenge for Scientific Discovery This repository is

Jungwoo Park 44 Dec 03, 2022
A lightweight python AUTOmatic-arRAY library.

A lightweight python AUTOmatic-arRAY library. Write numeric code that works for: numpy cupy dask autograd jax mars tensorflow pytorch ... and indeed a

Johnnie Gray 62 Dec 27, 2022
Underwater industrial application yolov5m6

This project wins the intelligent algorithm contest finalist award and stands out from over 2000teams in China Underwater Robot Professional Contest, entering the final of China Underwater Robot Prof

8 Nov 09, 2022
A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

img_sussifier A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196 Examples How to use install python pip i

41 Sep 30, 2022
Pytorch cuda extension of grid_sample1d

Grid Sample 1d pytorch cuda extension of grid sample 1d. Since pytorch only supports grid sample 2d/3d, I extend the 1d version for efficiency. The fo

lyricpoem 24 Dec 03, 2022
A Traffic Sign Recognition Project which can help the driver recognise the signs via text as well as audio. Can be used at Night also.

Traffic-Sign-Recognition In this report, we propose a Convolutional Neural Network(CNN) for traffic sign classification that achieves outstanding perf

Mini Project 64 Nov 19, 2022
DTCN IJCAI - Sequential prediction learning framework and algorithm

DTCN This is the implementation of our paper "Sequential Prediction of Social Me

Bobby 2 Jan 24, 2022
thundernet ncnn

MMDetection_Lite 基于mmdetection 实现一些轻量级检测模型,安装方式和mmdeteciton相同 voc0712 voc 0712训练 voc2007测试 coco预训练 thundernet_voc_shufflenetv2_1.5 input shape mAP 320

DayBreak 39 Dec 05, 2022
Lab course materials for IEMBA 8/9 course "Coding and Artificial Intelligence"

IEMBA 8/9 - Coding and Artificial Intelligence Dear IEMBA 8/9 students, welcome to our IEMBA 8/9 elective course Coding and Artificial Intelligence, t

Artificial Intelligence & Machine Learning (AI:ML Lab) @ HSG 1 Jan 11, 2022
Nicholas Lee 3 Jan 09, 2022
Label Hallucination for Few-Shot Classification

Label Hallucination for Few-Shot Classification This repo covers the implementation of the following paper: Label Hallucination for Few-Shot Classific

Yiren Jian 13 Nov 13, 2022
This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

[CVPRW 2021] - Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation

Anirudh S Chakravarthy 6 May 03, 2022
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds

PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds PCAM: Product of Cross-Attention Matrices for Rigid Registration of P

valeo.ai 24 May 31, 2022
FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks Image Classification Dataset: Google Landmark, COCO, ImageNet Model: Efficient

FedML-AI 62 Dec 10, 2022
Repository sharing code and the model for the paper "Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes"

Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes Setup virtualenv -p python3 venv source venv/bin/activate pip instal

Planet AI GmbH 9 May 20, 2022
Transferable Unrestricted Attacks, which won 1st place in CVPR’21 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet.

Transferable Unrestricted Adversarial Examples This is the PyTorch implementation of the Arxiv paper: Towards Transferable Unrestricted Adversarial Ex

equation 16 Dec 29, 2022
Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.

Multi-Time Attention Networks (mTANs) This repository contains the PyTorch implementation for the paper Multi-Time Attention Networks for Irregularly

The Laboratory for Robust and Efficient Machine Learning 68 Dec 17, 2022
FaceAnon - Anonymize people in images and videos using yolov5-crowdhuman

Face Anonymizer Blur faces from image and video files in /input/ folder. Require

22 Nov 03, 2022
A ssl analyzer which could analyzer target domain's certificate.

ssl_analyzer A ssl analyzer which could analyzer target domain's certificate. Analyze the domain name ssl certificate information according to the inp

vincent 17 Dec 12, 2022