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
Implementing Vision Transformer (ViT) in PyTorch

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

2 Dec 24, 2021
Colab notebook for openai/glide-text2im.

GLIDE text2im on Colab This repository provides a Colab notebook to produce images conditioned on text prompts with GLIDE [1]. Usage Run text2im.ipynb

Wok 19 Oct 19, 2022
Kindle is an easy model build package for PyTorch.

Kindle is an easy model build package for PyTorch. Building a deep learning model became so simple that almost all model can be made by copy and paste from other existing model codes. So why code? wh

Jongkuk Lim 77 Nov 11, 2022
An NVDA add-on to split screen reader and audio from other programs to different sound channels

An NVDA add-on to split screen reader and audio from other programs to different sound channels (add-on idea credit: Tony Malykh)

Joseph Lee 7 Dec 25, 2022
Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling Introduction This repository is code release for OctField: Hierarchical Implicit Fun

55 Dec 08, 2022
✨风纪委员会自动投票脚本，利用Github Action帮你进行裁决操作（为了让其他风纪委员有案件可判，本程序从中午12点才开始运行，有需要请自己修改运行时间）

风纪委员会自动投票 本脚本通过使用Github Action来实现B站风纪委员的自动投票功能，喜欢请给我点个STAR吧！ 如果你不是风纪委员，在符合风纪委员申请条件的情况下，本脚本会自动帮你申请 投票时间是早上八点，如果有需要请自行修改.github/workflows/Judge.yml中的时间，

Pesy Wu 25 Feb 17, 2021
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
Turning SymPy expressions into PyTorch modules.

sympytorch A micro-library as a convenience for turning SymPy expressions into PyTorch Modules. All SymPy floats become trainable parameters. All SymP

Patrick Kidger 89 Dec 13, 2022
Chinese Advertisement Board Identification(Pytorch)

Chinese-Advertisement-Board-Identification. We use YoloV5 to extract the ROI of the location of the chinese word. Next, we sort the bounding box and recognize every chinese words which we extracted.

Li-Wei Hsiao 12 Jul 21, 2022
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

Li Yang 1.1k Dec 19, 2022
SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning

SPCL SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning Update on 2021/11/25: ArXiv Ver

Binhui Xie (谢斌辉) 11 Oct 29, 2022
CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces This is a repository for the following pape

17 Oct 13, 2022
Virtual Dance Reality Stage is a feature that offers you to share a stage with another user virtually.

Virtual Dance Reality Stage is a feature that offers you to share a stage with another user virtually. It uses the concept of Image Background Removal using DeepLab Architecture (based on Semantic Se

Devashi Choudhary 5 Aug 24, 2022
Food recognition model using convolutional neural network & computer vision

Food recognition model using convolutional neural network & computer vision. The goal is to match or beat the DeepFood Research Paper

Hemanth Chandran 1 Jan 13, 2022
Implementation of ICLR 2020 paper "Revisiting Self-Training for Neural Sequence Generation"

Self-Training for Neural Sequence Generation This repo includes instructions for running noisy self-training algorithms from the following paper: Revi

Junxian He 45 Dec 31, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

108 Dec 17, 2022
efficient neural audio synthesis in the waveform domain

neural waveshaping synthesis real-time neural audio synthesis in the waveform domain paper • website • colab • audio by Ben Hayes, Charalampos Saitis,

Ben Hayes 169 Dec 23, 2022
App for identification of various objects. Based on YOLO v4 tiny architecture

Object_detection Repository containing trained model yolo v4 tiny, which is capable of identification 80 different classes Default feed is set to be a

Mateusz Kurdziel 0 Jun 22, 2022
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".

Rule-based Representation Learner This is a PyTorch implementation of Rule-based Representation Learner (RRL) as described in NeurIPS 2021 paper: Scal

Zhuo Wang 53 Dec 17, 2022