Sectionboxes_Malkeetv1 Object Detection Dataset and Pre-Trained Model by Section Boxes

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Explore Dataset

Here are a few use cases for this project:

Invoice Analysis Software: The model can be integrated into an automated invoice processing system to identify and extract details like vendor name, table of charges, barcodes, etc. This would significantly speed up the invoice processing and accounting tasks in businesses.
Document Management Systems: The model can be used to automate document classification and segmentation in a digital document management system. It can help categorize various sections of documents like paragraphs, tables, logos, etc., facilitating faster searches and better document organization.
Content Management Systems: The model can be employed to enhance CMS software by automatically identifying and tagging components of a uploaded content, such as logos, barcodes, QR codes and graphs, streamlining the image searching and sorting process for users.
Quality Control in Print Industries: The model can be utilized for automating quality checks in print and publishing houses. It can identify, verify, and ensure the right placement of elements like vendors' names, logos, barcodes, QR codes and text alignment on printed materials.
Intellectual Property Protection: The model could be employed in the detection of unauthorized use of logos or vendor names in documents, contributing to efforts to prevent counterfeiting or misuse of brand identities.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

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

@misc{
                            sectionboxes_malkeetv1_dataset,
                            title = { Sectionboxes_Malkeetv1 Dataset },
                            type = { Open Source Dataset },
                            author = { Section Boxes },
                            howpublished = { \url{ https://universe.roboflow.com/section-boxes/sectionboxes_malkeetv1 } },
                            url = { https://universe.roboflow.com/section-boxes/sectionboxes_malkeetv1 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { feb },
                            note = { visited on 2024-04-26 },
                            }