update readme

README.md

@@ -219,112 +219,91 @@ The fields for the COCO config:
   - `category_id`: label for the image
   - `image_id`: id for the image
   - `iscrowd`: COCO is crowd flag
-  - `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts
+  - `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
 
 
  
 ### Data Splits
 
-Describe and name the splits in the dataset if there are more than one.
+The dataset contains a train, validation and test split with the following numbers per split:
 
-Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
 
-Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length.  For example:
+|                 | train | validation | test |
+|-----------------|------:|-----------:|-----:|
+| Input Sentences |   196 |         22 |  135 |
 
-|                         | train | validation | test |
-|-------------------------|------:|-----------:|-----:|
-| Input Sentences         |       |            |      |
-| Average Sentence Length |       |            |      |
 
 ## Dataset Creation
 
+> [this] dataset was produced using a single source, the Lectaurep Repertoires dataset [Rostaing et al., 2021], which served as a basis for only the training and development split. The testset is composed of original data, from various documents, from the 17th century up to the early 20th with a single soldier war report. The test set is voluntarily very different and out of domainwith column borders that are not drawn nor printed in certain cases, layout in some kind of masonry layout. p.8
+.
 ### Curation Rationale
 
-What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?
+This dataset was created to produce a simplified version of the [Lectaurep Repertoires dataset](https://github.com/HTR-United/lectaurep-repertoires) which was found to contain:
 
-### Source Data
+> around 16 different ways to describe columns, from Col1 to Col7, the case-different col1-col7 and finally ColPair and ColOdd, which we all reduced to Col p.8
 
-This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
+### Source Data
 
 #### Initial Data Collection and Normalization
 
-Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
-
-If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
+The LECTAUREP (LECTure Automatique de REPertoires) project, which began in 2018, is a joint initiative of the Minutier central des notaires de Paris, the National Archives and the 
+ Minutier central des notaires de Paris of the National Archives, the [ALMAnaCH (Automatic Language Modeling and Analysis & Computational Humanities)](https://www.inria.fr/en/almanach) team at Inria and the EPHE (Ecole Pratique des Hautes Etudes), in partnership with the Ministry of Culture.
 
-If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
+> The lectaurep-bronod corpus brings together 100 pages from the repertoire of Maître Louis Bronod (1719-1765), notary in Paris from December 13, 1719 to July 23, 1765. The pages concerned were written during the years 1742 to 1745.
 
 #### Who are the source language producers?
 
-State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.
-
-If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
-
-Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
-
-Describe other people represented or mentioned in the data. Where possible, link to references for the information.
+[More information needed]
 
 ### Annotations
 
-If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
+|          | Train | Dev | Test | Total | Average area | Median area |
+|----------|-------|-----|------|-------|--------------|-------------|
+| Col      | 724   | 105 | 829  | 1658  | 9.32         | 6.33        |
+| Header   | 103   | 15  | 42   | 160   | 6.78         | 7.10        |
+| Marginal | 60    | 8   | 0    | 68    | 0.70         | 0.71        |
+| Text     | 13    | 5   | 0    | 18    | 0.01         | 0.00        |
+|          |       |     | -    |       |              |             |
+1
 
 #### Annotation process
 
-If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
+[More information needed]
 
 #### Who are the annotators?
 
-If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.
-
-Describe the people or systems who originally created the annotations and their selection criteria if applicable.
-
-If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
-
-Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
+[More information needed]
 
 ### Personal and Sensitive Information
 
-State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
-
-State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).  
-
-If efforts were made to anonymize the data, describe the anonymization process.
-
+This data does not contain information relating to living individuals. 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-Please discuss some of the ways you believe the use of this dataset will impact society.
-
-The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.
-
-Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.
+There are a growing number of datasets related to page layout for historical documents. This dataset offers a differnet approach to annotating these datasets (focusing on object detection rather than pixel level annotations).
 
 ### Discussion of Biases
 
-Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
-
-For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
-
-If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
+Historical documents contain a broad variety of page layouts this means that the ability for models trained on this dataset to transfer to documents which may contain very different layouts is not certain. 
 
 ### Other Known Limitations
 
-If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
+[More information needed]
+
 
 ## Additional Information
 
 ### Dataset Curators
 
-List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
 
 ### Licensing Information
 
-Provide the license and link to the license webpage if available.
+[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
 
 ### Citation Information
 
-
 ```
 @dataset{clerice_thibault_2022_6827706,
   author       = {Clérice, Thibault},
@@ -338,8 +317,12 @@ Provide the license and link to the license webpage if available.
 }
 ```
 
-If the dataset has a [DOI](https://doi.org/10.5281/zenodo.6827706), please provide it here.
 
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6827706.svg)](https://doi.org/10.5281/zenodo.6827706)
+
+
+    
 ### Contributions
 
 Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.