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
scAR (single-cell Ambient Remover) is a package for data denoising in single-cell omics.

scAR scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA

19 Nov 28, 2022
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]

RTFM This repo contains the Pytorch implementation of our paper: Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Lear

Yu Tian 242 Jan 08, 2023
Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task

multi-task_losses_optimizer Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task 已经实验过了,不会有cuda out of memory情况 ##Par

14 Dec 25, 2022
Simple torch.nn.module implementation of Alias-Free-GAN style filter and resample

Alias-Free-Torch Simple torch module implementation of Alias-Free GAN. This repository including Alias-Free GAN style lowpass sinc filter @filter.py A

이준혁(Junhyeok Lee) 64 Dec 22, 2022
Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

Spatio-Temporal Entropy Model A Pytorch Reproduction of Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression. More details can

16 Nov 28, 2022
CLOOB training (JAX) and inference (JAX and PyTorch)

cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train

Katherine Crowson 64 Nov 27, 2022
Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.

Decision Transformer Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas†, and Igor M

Kevin Lu 1.4k Jan 07, 2023
Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

DeepXML Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents Architectures and algorithms DeepXML supports

Extreme Classification 49 Nov 06, 2022
Code for our CVPR 2021 paper "MetaCam+DSCE"

Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification (CVPR'21) Introduction Code for our CVPR 2021

FlyingRoastDuck 59 Oct 31, 2022
An implementation of the BADGE batch active learning algorithm.

Batch Active learning by Diverse Gradient Embeddings (BADGE) An implementation of the BADGE batch active learning algorithm. Details are provided in o

125 Dec 24, 2022
Code for the paper "Location-aware Single Image Reflection Removal"

Location-aware Single Image Reflection Removal The shown images are provided by the datasets from IBCLN, ERRNet, SIR2 and the Internet images. The cod

72 Dec 08, 2022
Code for paper Adaptively Aligned Image Captioning via Adaptive Attention Time

Adaptively Aligned Image Captioning via Adaptive Attention Time This repository includes the implementation for Adaptively Aligned Image Captioning vi

Lun Huang 45 Aug 27, 2022
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

ROCKET + MINIROCKET ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge D

298 Dec 26, 2022
An introduction to bioimage analysis - http://bioimagebook.github.io

Introduction to Bioimage Analysis This book tries explain the main ideas of image analysis in a practical and engaging way. It's written primarily for

Bioimage Book 20 Nov 28, 2022
Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

UTNet (Accepted at MICCAI 2021) Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation Introduction Transf

110 Jan 01, 2023
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 03, 2023
Bayesian Generative Adversarial Networks in Tensorflow

Bayesian Generative Adversarial Networks in Tensorflow This repository contains the Tensorflow implementation of the Bayesian GAN by Yunus Saatchi and

Andrew Gordon Wilson 1k Nov 29, 2022
A Lightweight Experiment & Resource Monitoring Tool 📺

Lightweight Experiment & Resource Monitoring 📺 "Did I already run this experiment before? How many resources are currently available on my cluster?"

170 Dec 28, 2022
A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/15

Pradyumna Reddy Chinthala 190 Dec 15, 2022
Source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals.

PatchGraph This repository contains the source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals. Installation Creat

Paloma Sodhi 11 Dec 15, 2022