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ShareScore release 0.9.0
Dataset results
5 results for “document recognition”
Transkribus - Handwritten Text Recognition for Premodern Documents (SIMS 2020 Lightning Talk)
<p>Transkribus is a platform for text recognition and can be used via the Transkribus Expert Software (available after registration: transkribus.eu). Through Transkribus different tools for document analysis and text recognition can be directly applied. The intro demonstrates very briefly how Transkribus can help with regards to premodern documents especially since a variety of pre-trained models are already available: for Latin (prints and handwriting), for early modern vernaculars in French, Dutch, English, and German. For more information go to transkribus.eu.</p> <p>Presented as a Schoenberg Symposium 2020 Lightning Talk</p>
Benchmark for the evaluation of named entity recognition over ancient documents
<p>The dataset consists of a multilingual noisy corpora for named entity recognition (NER).<br> The noisy versions are simulated from the CoNLL-02 (Spanish and Dutch) and CoNLL-03 (English) NER corpora.<br> The original collections are re-OCRed and four types of noises at two different levels are added in order to simulate various OCR output.</p> <p>More precisely, we first extracted raw texts and converted them into images. These images have been contaminated by adding some common noises when using a scanner. We further extract OCRed data using tesseract open source<br> OCR engine v-3.04.01. Consequently to the image noise insertions, OCRed data contains degradations. Original and noisy texts are finally aligned.</p> <p>This archive contains three folders (one per language). The folders contain the degraded images, the noisy texts extracted by the OCR and their aligned version with clean data.</p> <p>These are the supplementary materials for the TPDL 2020 paper <a href="https://zenodo.org/record/4734376#.YJKAcKE6-Uk">Assessing and minimizing the impact of OCR quality on named entity recognition</a>. If you end up using whole or parts of this resource,<br> please cite this paper:</p> <pre><code>@InProceedings{10.1007/978-3-030-54956-5_7, author="Hamdi, Ahmed and Jean-Caurant, Axel and Sid{\`e}re, Nicolas and Coustaty, Micka{\"e}l and Doucet, Antoine", editor="Hall, Mark and Mer{\v{c}}un, Tanja and Risse, Thomas and Duchateau, Fabien", title="Assessing and Minimizing the Impact of OCR Quality on Named Entity Recognition", booktitle="Digital Libraries for Open Knowledge", year="2020", publisher="Springer International Publishing", address="Cham", pages="87--101", isbn="978-3-030-54956-5" }</code></pre> <p><strong>Acknowledgments</strong><br> This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant 770299 [NewsEye](https://www.newseye.eu/).</p>
Oficio de Hipotecas de Girona. A dataset of Spanish notarial deeds (18th Century) for Handwritten Text Recognition and Layout Analysis of historical documents.
<p>This dataset is a subset of 596 documents from the <em>Registre d'Hipoteques de Girona</em> of 1769 collection, guarded by the <a href="http://xac.gencat.cat/ca/llista_arxius_comarcals/girones/"><em>Arxiu Històric de Girona</em></a>. This collection, is composed by hundreds of thousands of notarial deeds from the XVIII-XIX century (1768-1862). Sales, redemption of censuses, inheritance and matrimonial chapters are among the most common documentary typologies in the collection.</p> <p>This dataset is composed of more than 23700 text lines written by a single hand, covering more that 50 different topics (documentary typologies) and a vocabulary of more than 2400 different words. The documents are transcribed using the so-called diplomatic criteria. Additionally, transcripts were tagged with <br> extra enriching/complementary information (e.g. expansion of the abbreviations, hyphen marks, etc.). Along with the transcripts the layout of the document is detected and recorded. Pages have been labeled using six different layout regions.</p> <p>The images along with their respective ground-truth was compiled in PAGE compliant XML format<br> by the <a href="http://www2.udg.edu/tabid/11296/Default.aspx"><em>Centre de Recerca d'Història Rural</em></a> and the HTR group of the <a href="https://www.prhlt.upv.es">Pattern Recognition and Human Language Technologies Research Center</a>.</p>
Dataset for Paper: A System for Processing and Recognition of Greek Byzantine and Post-Byzantine Documents
<p>Dataset for the paper: "A System for Processing and Recognition of Greek Byzantine and Post-Byzantine Documents", P. Kaddas, K. Palaiologos, B. Gatos, V. Katsouros, K. Christopoulou, 17th International Conference on Document Analysis and Recognition (ICDAR), San Jose, California, USA</p> <p>The dataset consists of 57 pages from the third edition of the Greek New Testament published by Robert Estienne (1503–1559), who was appointed “Royal Typographer” by the King of France François I (1494–1547). Robert Estienne produced this edition in 1550 using the grecs du roi typeface, produced by Claude Garamont on the basis of the Greek minuscule style of the calligrapher Angelos Vergikios (1505–1569) from Crete, who active copying Greek manuscripts in Venice and France. The dataset consists of 2045 cropped text line images in .png format with their corresponding OCR in .txt format, where 1431 used for training, 204 for validation and 410 for test. Initial images acquired from: https://bibles-online.net/flippingbook/1550/</p>
Dataset for Paper: Text Line Detection and Recognition of Greek Polytonic Documents
<p>Dataset for Paper: Text Line Detection and Recognition of Greek Polytonic Documents, P. Kaddas, B. Gatos, K. Palaiologos, K. Christopoulou and K. Kritsis, 4th Workshop on Machine Learning (WML), San Jose, California, USA</p> <p>We introduce a new dataset, named GTLD-small dataset, with annotated text line quadrilateral polygons of 1.642 documents, including annotations on 3 datasets (Tobacco-3482, PIOP and ShakeIT dataset)</p> <table> <caption>Overview of the datasets included in this work and the number of images used for training, validation and testing.</caption> <thead> <tr> <th scope="col">Collection</th> <th scope="col">#Total</th> <th scope="col">#train</th> <th scope="col">#val</th> <th scope="col">#test</th> </tr> </thead> <tbody> <tr> <td>PIOP-small</td> <td>950</td> <td>672</td> <td>90</td> <td>188</td> </tr> <tr> <td>ShakeIT-small</td> <td>357</td> <td>264</td> <td>27</td> <td>66</td> </tr> <tr> <td>Tobacco-3482-small</td> <td>335</td> <td>240</td> <td>30</td> <td>65</td> </tr> </tbody> </table> <p> </p>
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