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11 results for “Handwriting Dataset”
Handwriting of the papal chancery and beyond: Dataset
<p><strong>Handwriting of the papal chancery and beyond: Dataset</strong></p> <p>D. Stutzmann</p> <p>The original data was originally produced in Oct.-Nov. 2019 for the paper "Les écritures de la chancellerie pontificale dans le paysage européen (XIIe–XVe siècles)" at the conference "Les actes pontificaux. Un trésor à exploiter" held at the German Historical Institute in Paris on Nov. 28th, 2019.</p> <p>The text of the paper is published on HAL: <a href="https://hal.archives-ouvertes.fr/halshs-03628606">https://hal.archives-ouvertes.fr/halshs-03628606</a>. Parts of the present corpus (preliminary code and partial data) were released on Github on Jan. 4th, 2022 (<a href="https://github.com/oriflamms/RegVat_ArchNatJJ_MOM_LBA_2019">https://github.com/oriflamms/RegVat_ArchNatJJ_MOM_LBA_2019</a>).</p> <p>The study has been extended and deepened for a more comprehensive publication as an article in the proceedings.</p> <p>The analyzed corpora encompass images from</p> <ul> <li>Registra Vaticana (Vaticano, Archivio Apostolico, Reg. Vat.)</li> <li>Registers of French royal chancery (Paris, Archives Nationales, JJ series)</li> <li>images from Monasterium.net and Lichtbildarchiv</li> </ul> <p>The present dataset does not contain the images.</p> <p>The present dataset contains</p> <ol> <li>folder /_initial_corpus/: metadata on the "original corpus" that I intended to use</li> <li>folder /data/: (a) the original output data as produced by the Computer Vision library processing the images; (b) the data with its metadata.</li> <li>folder /rds/: RDS files produced as part of the analysis with the R software (cf. https://www.r-project.org/)</li> <li>folder /images/: figures and illustrations of the article</li> <li>folder /plotly/: corresponding interactive visualisations of the figures</li> </ol> <p>At the root, along witht this README file, the R code to produce the statistics, RDS files and images.</p> <p>For this library, see Nicolaou, Anguelos, A. D. Bagdanov, Marcus Liwicki, et D. Karatzas. « Sparse radial sampling lbp for writer identification ». In 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 716-20, 2015., cf. <a href="https://doi.org/10.48550/arXiv.1504.06133">https://doi.org/10.48550/arXiv.1504.06133</a></p>
BanglaWriting Words Dataset: A Collection of Isolated Word Images from the BanglaWriting multi-purpose Bangla offline-handwriting dataset (WoBW)
<p>The WoBW (Words from BanglaWriting) dataset is a curated collection of isolated word images, adapted from the original BanglaWriting dataset (url: https://data.mendeley.com/datasets/r43wkvdk4w/1).</p> <p>WoBW focuses on individual words extracted from handwritten Bangla text samples in the BanglaWriting corpus, making it a valuable resource for research in word-level Bangla handwriting recognition and related natural language processing tasks.</p> <p>Mridha, Dr. M. F.; Quwsar Ohi, Abu; Ali, M. Ameer; Emon, Mazedul Islam; Kabir, Md Mohsin (2020), “BanglaWriting: A multi-purpose offline Bangla handwriting dataset”, Mendeley Data, V1, doi: 10.17632/r43wkvdk4w.1</p>
POPP Datasets : Datasets for handwriting recognition from French population census
<p><strong>POPP datasets</strong></p> <p>This repository contains 3 datasets created within the POPP project (<a href="https://popp.hypotheses.org/#ancre2">Project for the Oceration of the Paris Population Census</a>) for the task of handwriting text recognition. These datasets have been published in <a href="https://hal.science/hal-03675614/"><em>Recognition and information extraction in historical handwritten tables: toward understanding early 20th century Paris census</em> at DAS 2022.</a></p> <p>The 3 datasets are called “Generic dataset”, “Belleville”, and “Chaussée d’Antin” and contains lines made from the extracted rows of census tables from 1926. Each table in the Paris census contains 30 rows, thus each page in these datasets corresponds to 30 lines.</p> <p>The structure of each dataset is the following:</p> <ul> <li>double-pages : images of the double pages</li> <li>pages: <ul> <li>images: images of the pages</li> <li>xml: METS and ALTO files of each page containing the coordinates of the bounding boxes of each line</li> </ul> </li> <li>lines: contains the labels in the file <code>labels.json</code> and the line images splitted into the folders <em>train</em>, <em>valid</em> and <em>test</em>. The double pages were scanned at a resolution of 200dpi and saved as PNG images with 256 gray levels. The line and page images are shared in the TIFF format, also with 256 gray levels.</li> </ul> <p>Since the lines are extracted from table rows, we defined 4 special characters to describe the structure of the text:</p> <ul> <li>¤ : indicates an empty cell</li> <li>/ : indicates the separation into columns</li> <li>? : indicates that the content of the cell following this symbol is written above the regular baseline</li> <li>! : indicates that the content of the cell following this symbol is written below the regular baseline</li> </ul> <p>We provide a script <code>format_dataset.py</code> to define which special character you want to use in the ground-truth.</p> <p>The split for the <em>Generic Dataset</em> and <em>Belleville</em> have been made at the double-page level so that each writer only appears in one subset among train, evaluation and test. The following table summarizes the splits and the number of writers for each dataset:</p> <table> <thead> <tr> <th>Dataset</th> <th>train - # of lines</th> <th>validation - # of lines</th> <th>test - # of lines</th> <th># of writers</th> </tr> </thead> <tbody> <tr> <td>Generic</td> <td>3840 (128 pages)</td> <td>480 (16 pages)</td> <td>480 (16 pages)</td> <td>80</td> </tr> <tr> <td>Belleville</td> <td>1140 (38 pages)</td> <td>150 (5 pages)</td> <td>180 (6 pages)</td> <td>1</td> </tr> <tr> <td>Chaussée d’Antin</td> <td>625</td> <td>78</td> <td>77</td> <td>10</td> </tr> </tbody> </table> <p> </p> <p><strong>Generic dataset (or POPP dataset)</strong></p> <ul> <li>This dataset is made 4800 annotated lines extracted from 80 double pages of the 1926 Paris census.</li> <li>There is one double page for each of the 80 districts of Paris</li> <li>There is one writer per double page so the dataset contains 80 different writers.</li> </ul> <p> </p> <p><strong>Belleville dataset</strong></p> <p>This dataset is a mono-writer dataset made of 1470 lines (49 pages) from the <em>Belleville</em> district census of 1926.</p> <p> </p> <p><strong>Chaussée d’Antin dataset</strong></p> <p>This dataset is a multi-writer dataset made of 780 lines (26 pages) from the <em>Chaussée d’Antin</em> district census of 1926 and written by 10 different writers.</p> <p> </p> <p><strong>Error reporting</strong></p> <p>It is possible that errors persist in the ground truth, so any suggestions for correction are welcome. To do so, please make a merge request on the <a href="https://github.com/Shulk97/POPP-datasets">Github repository</a> and include the correction in both the labels.json file and in the XML file concerned.</p> <p> </p> <p><strong>Citation Request</strong></p> <p>If you publish material based on this database, we request you to include a reference to paper <a href="http://link.springer.com/chapter/10.1007/978-3-031-06555-2_10"><code>T. Constum, N. Kempf, T. Paquet, P. Tranouez, C. Chatelain, S. Brée, and F. Merveille,Recognition and information extraction in historical handwritten tables: toward understanding early 20th century Paris census ,Document Analysis Systems (DAS), pp. 143- 157, La Rochelle, 2022.</code></a></p>
ICFHR 2016 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset
<p>The ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) provided a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition.</p> <p>If you use this dataset, please cite:</p> <p>Florence Cloppet, Véronique Eglin, Van Cuong Kieu, Dominique Stutzmann, and Nicole Vincent, "ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script", <em>Proceedings of International Conference on Frontiers in Handwriting Recognition</em>, Los Alamos : IEEE, 2016, p. 590-595. [<a href="https://doi.org/10.1109/ICFHR.2016.0113">https://doi.org/10.1109/ICFHR.2016.0113</a>]</p> <p>At this competition, we proposed two independent classification tasks which attracted five participants with seven submitted classifiers. Those classifiers are trained on a set of 2000 images with their ground truths. In the first task of script crisp classification, the classifiers have been evaluated on a test set of 1000 single-type manuscripts. In the second task of “Fuzzy Classification”, the classifiers have been carried out on a set of 2000 multi-script-type manuscripts.</p> <p>The present dataset contains the training dataset, both test datasets (task 1 and task 2) and the matrices provided by the competitors. It was first published on a https://clamm.irht.cnrs.fr/icfhr2016-clamm/ in Oct. 2016.</p> <p> </p>
A Serious Game to Anticipate Handwriting Difficulties Screening Through Visual Perception Assessment - DATASET
<p>Each row in the dataset represents a subject. It contains:</p> <ul> <li>The answers to a characterization questionnaire</li> <li>The performance in the game described in the article</li> </ul>
Can Free Drawing Anticipate Handwriting Difficulties? A Longitudinal Study - DATASET
<p>Data to support the conference paper:</p> <p>Dui, L. G., Toffoli, S., Speziale, C., Termine, C., Matteucci, M., & Ferrante, S. (2022, September). Can Free Drawing Anticipate Handwriting Difficulties? A Longitudinal Study. In <em>2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)</em> (pp. 1-4). IEEE.</p> <ul> <li>BHI22_risk.xlsx: an Excel file with information about: <ul> <li>risk: the risk for handwriting delay, 0=no risk, 1=risk</li> <li>hand: right or left</li> <li>sex: M=male, F=female</li> <li>age: computed at the beginning of the longitudinal study</li> </ul> </li> <li>drawing_features.mat: a Matlab file with five datasets, one for each time point of the longitudinal study, with rows=children, columns=features</li> <li>metadata.mat: features names and type</li> </ul> <p> </p>
Digital Tools for Handwriting Proficiency Evaluation in Children - DATASET
<p>Data to support the findings in the conference paper</p> <p>L. G. Dui, E. Calogero, M. Malavolti, C. Termine, M. Matteucci and S. Ferrante, "Digital Tools for Handwriting Proficiency Evaluation in Children," <em>2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)</em>, Athens, Greece, 2021, pp. 1-4, doi: 10.1109/BHI50953.2021.9508539.</p> <ul> <li>Characterization.xls: an Excel file with sheets: <ul> <li>Characterization: <ul> <li>Subject ID</li> <li>School ID</li> <li>Class</li> <li>Age</li> <li>Sex</li> <li>Hand</li> <li>Years since starting writing in cursive</li> </ul> </li> <li>TabletSUS: score for the System Usability Scale referred to writing on tablet</li> <li>PenSUS: score for the System Usability Scale referred to writing on paper with the smart ink pen</li> <li>BVSCO-2: number of graphemes produced when writing on paper or tablet</li> </ul> </li> <li>pen_tablet_data.mat: a Matlab file with indicators and their names, computed with tablet and pen data</li> </ul>
ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset
<p>The ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) followed a competition at ICFHR2016 and provided a rich annotated database of European medieval manuscripts to the community on Handwriting Analysis and Recognition, containing information on date of production and class of script.</p> <p>If you use this upload, please cite:</p> <p>Florence Cloppet, Véronique Eglin, Marlène Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, "ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script", in <em>14th IAPR International Conference on Document Analysis and Recognition</em>. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. <a href="https://doi.org/10.1109/ICDAR.2017.224">https://doi.org/10.1109/ICDAR.2017.224</a></p> <p>We proposed four independent classification tasks which attracted 10 registered teams, with 6 submitted classifiers from 4 participants. Those classifiers are trained on a set of 3540 images with their ground<br> truths. In task 1 (Script classification) and task 3 (Date classification), the classifiers have been evaluated by a test set of 2000 greyscale, tiff, 300 dpi images. In task 2 (Script classification) and task 4 (Date classification), the test set consists of 1000 images in different formats, resolutions and color<br> representation.</p> <p>The present dataset contains the training dataset, both test datasets (tasks 1 and 3, and tasks 2 and 4) and the matrices provided by the competitors. It was first published on <a href="https://clamm.irht.cnrs.fr/icdar-2017/">https://clamm.irht.cnrs.fr/icdar-2017/</a> in Nov. 2017.</p>
Investigating Visual Perception Impairments through Serious Games and Eye Tracking to Anticipate Handwriting Difficulties - DATASET
<p>In the present dataset, each row represents a subject. For each subject, there are</p> <ul> <li>the ID</li> <li>the gender</li> <li>the class</li> <li>the results in the BVSCO-2 test</li> <li>their position over or under the BVSCO-2 threshold ("prove sopra soglia" represents the number of exercises in which the subject was over the thresold, and "sopra soglia generale" is 1 when a subject is over the threshold in all of the exercises, and 0 otherwise)</li> <li>the features extracted from the game described in the article (times and errors)</li> <li>the features extracted from the data produced by drawing with the Apple Pencil</li> <li>the features extracted from the eye tracker. </li> </ul>
GoBo - A Handwriting Recognition dataset for Personalization
<p>This dataset comprises the images for the personalization described in the paper <em>Personalizing Handwriting Recognition Systems with Limited User-Specific Samples</em>.<br> </p> <p>Dataset Statistics (v.1.0)</p> <p>* Handwritten word-level images<br> * English<br> * 40 Participants<br> * 5 sets from different sources for personalization <br> * 2 sets from 2 domains (same domains as 2 personalization sets) for testing<br> * 926 words/writer, 37k words in total<br> <br> More details can be found on the Github Repository:<br> <a href="https://github.com/catalpa-cl/GoBo/">Github GoBo</a><br> <br> <br> Model<br> gobo_Baselinemodel.hdf5</p>
Urdu Handwriting Dataset for Demographic Traits Classification
<p>Urdu Handwriting Dataset for Demographic Traits Classification was developed in Bahria University, Islamabad, Pakistan as a part of the bachelor's degree final year thesis/project. This is a unique dataset which is the first of its kind. The dataset is composed of 1000 unique handwriting images each taken from unique individuals. It can be seen in the title, the handwriting samples are specifically in Urdu Language. Urdu Handwriting Dataset is made for the Classification of Demographic Traits problem due to which it consists of the demographic information of each individual. Following are the demographic traits that are covered in the dataset:</p> <ol> <li>Gender (Male, Female)</li> <li>Handedness (Left, Right)</li> <li>Age-Group (15-20,21-30,31-40,41-50,51-up)</li> <li>Province (Balochistan, Sindh, Punjab, kpk, gilgit-baltistan, none)</li> <li>Occupation (Student, Employee, Both, None)</li> <li>Education (Primary(Below Matriculation), Matriculation, Intermediate, Bachelors, Masters, PHD, None)</li> </ol>
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