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
Rotary Transformer

[中文|English] Rotary Transformer Rotary Transformer is an MLM pre-trained language model with rotary position embedding (RoPE). The RoPE is a relative

325 Jan 03, 2023
Implementation of neural class expression synthesizers

NCES Implementation of neural class expression synthesizers (NCES) Installation Clone this repository: https://github.com/ConceptLengthLearner/NCES.gi

NeuralConceptSynthesis 0 Jan 06, 2022
PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

FInite volume Neural Network (FINN) This repository contains the PyTorch code for models, training, and testing, and Python code for data generation t

Cognitive Modeling 20 Dec 18, 2022
Implementation of Hourglass Transformer, in Pytorch, from Google and OpenAI

Hourglass Transformer - Pytorch (wip) Implementation of Hourglass Transformer, in Pytorch. It will also contain some of my own ideas about how to make

Phil Wang 61 Dec 25, 2022
Yet Another Robotics and Reinforcement (YARR) learning framework for PyTorch.

Yet Another Robotics and Reinforcement (YARR) learning framework for PyTorch.

Stephen James 51 Dec 27, 2022
code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

Code for our ECCV (2020) paper A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation. Prerequisites: python == 3.6.8 pytorch ==1.1.0

32 Nov 27, 2022
Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences"

Syntax-Customized-Video-Captioning Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences". This is my second w

3 Dec 05, 2022
Video-face-extractor - Video face extractor with Python

Python face extractor Setup Create the srcvideos and faces directories Put your

2 Feb 03, 2022
PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL: Graph Contrastive Learning for PyTorch PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL com

GCL: Graph Contrastive Learning Library for PyTorch 594 Jan 08, 2023
Ray tracing of a Schwarzschild black hole written entirely in TensorFlow.

TensorGeodesic Ray tracing of a Schwarzschild black hole written entirely in TensorFlow. Dependencies: Python 3 TensorFlow 2.x numpy matplotlib About

5 Jan 15, 2022
Code for technical report "An Improved Baseline for Sentence-level Relation Extraction".

RE_improved_baseline Code for technical report "An Improved Baseline for Sentence-level Relation Extraction". Requirements torch = 1.8.1 transformers

Wenxuan Zhou 74 Nov 29, 2022
Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph

NIRPS-ETC Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph February 2

Nolan Grieves 2 Sep 15, 2022
Randomizes the warps in a stock pokeemerald repo.

pokeemerald warp randomizer Randomizes the warps in a stock pokeemerald repo. Usage Instructions Install networkx and matplotlib via pip3 or similar.

Max Thomas 6 Mar 17, 2022
Continuum Learning with GEM: Gradient Episodic Memory

Gradient Episodic Memory for Continual Learning Source code for the paper: @inproceedings{GradientEpisodicMemory, title={Gradient Episodic Memory

Facebook Research 360 Dec 27, 2022
PyTorch implementation of Convolutional Neural Fabrics http://arxiv.org/abs/1606.02492

PyTorch implementation of Convolutional Neural Fabrics arxiv:1606.02492 There are some minor differences: The raw image is first convolved, to obtain

Anuvabh Dutt 25 Dec 22, 2021
Script utilizando OpenCV e modelo Machine Learning para detectar o uso de máscaras.

Reconhecendo máscaras Este repositório contém um script em Python3 que reconhece se um rosto está ou não portando uma máscara! O código utiliza da bib

Maria Eduarda de Azevedo Silva 168 Oct 20, 2022
Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

T-Few This repository contains the official code for the paper: "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learni

220 Dec 31, 2022
This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text"

Iconary This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text". It includes the

AI2 6 May 24, 2022
CPU inference engine that delivers unprecedented performance for sparse models

The DeepSparse Engine is a CPU runtime that delivers unprecedented performance by taking advantage of natural sparsity within neural networks to reduce compute required as well as accelerate memory b

Neural Magic 1.2k Jan 09, 2023
Recognize Handwritten Digits using Deep Learning on the browser itself.

MNIST on the Web An attempt to predict MNIST handwritten digits from my PyTorch model from the browser (client-side) and not from the server, with the

Harjyot Bagga 7 May 28, 2022