Name: CamComercio Dataset
License: https://choosealicense.com/licenses/mit/

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Explore Dataset

Here are a few use cases for this project:

Digitizing and Organizing Documents: CamComercio can be used to recognize and extract various elements from paper or digital documents, automatically converting them into searchable, structured formats. This can streamline the process of digitizing large archives, improve data storage, and ease the retrieval of data when needed.
Automatic Text Summarization: By identifying titles, headers, and sentences, CamComercio can be utilized to create a personalized summary of the content within the document. This could save time for users who need an overview of the information quickly, such as students, researchers, or business professionals.
Enhanced OCR Systems: CamComercio's ability to identify various segment classes can be integrated into Optical Character Recognition (OCR) systems, enhancing their accuracy and performance. This would enable better extraction of texts from documents, especially for those with complex layouts or non-standard formatting.
Assisted Accessibility: CamComercio can be employed to improve accessibility for visually impaired users by recognizing and properly labeling various document components. This would help in generating accessible content for screen readers or other assistive technologies, providing an inclusive experience for those with visual disabilities.
Template Creation and Analysis: By detecting specific segments such as titles, headers, and footers, CamComercio can contribute to the development of templates for efficient document creation and analysis. It can recognize patterns and structures across multiple documents, assisting users in adapting and conforming new content to established guidelines and formatting standards.

Cite This Project

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

@misc{
                            camcomercio_dataset,
                            title = { CamComercio Dataset },
                            type = { Open Source Dataset },
                            author = { DociaPublic },
                            howpublished = { \url{ https://universe.roboflow.com/dociapublic/camcomercio } },
                            url = { https://universe.roboflow.com/dociapublic/camcomercio },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { sep },
                            note = { visited on 2024-04-19 },
                            }