document segmenter Computer Vision Project

fichesmat

Updated a year ago

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Classes (10)
adresse
affectation
dates
decision
details
etat_civil
instruction
nom
num_mat
signalement

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Description

Here are a few use cases for this project:

  1. Historical Document Analysis: Researchers and historians can use the document segmenter to identify, classify, and organize information from various types of historical documents, including handwritten letters and manuscripts, easing the task of extracting and analyzing data while preserving valuable archives.

  2. Personal Identification Verification: Governments and organizations can utilize the document segmenter to streamline the processing of identity documents (like passports or driver's licenses) by extracting relevant information such as name, date of birth, address, and other details, resulting in improved efficiency in application processing and background checks.

  3. Legal Document Processing: Law firms and legal departments can leverage the document segmenter to parse and classify data from court documents and contracts, assisting in case preparation by quickly identifying relevant sections and specific clauses.

  4. Public Record Management: The document segmenter can help in digitizing, cataloging, and retrieving public records (like land registries, marriage certificates, and birth/death records) to create a more efficient and accessible system for public administration and citizen services.

  5. Automated Data Extraction for Research: Researchers in various fields can use the document segmenter to automatically extract and classify data from hard-to-read publications or databases that have been poorly digitized, saving considerable time and effort while performing literature reviews or data analysis.

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            document-segmenter_dataset,
                            title = { document segmenter Dataset },
                            type = { Open Source Dataset },
                            author = { fichesmat },
                            howpublished = { \url{ https://universe.roboflow.com/fichesmat/document-segmenter } },
                            url = { https://universe.roboflow.com/fichesmat/document-segmenter },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { dec },
                            note = { visited on 2024-12-22 },
                            }
                        
                    

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