IDP1 Object Detection Dataset and Pre-Trained Model by Edvard

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Explore Dataset

Here are a few use cases for this project:

Legal Proceedings: The IDP1 model can be critical for identifying stamps and signatures on legal documents. Lawyers, judges, and court clerks could use this model to scan and verify whether a certain document contains necessary stamps and signatures, which will enhance the speed of legal procedures.
Fraud Detection: Banks and financial institutions can deploy this model to detect potential frauds in financial documents like cheques, loan agreements, etc., by checking for missing or fraudulent signatures or stamps.
Document Processing System: The model can be used in the automation of paper-based office workflows. It can assist an AI-driven document processing system by identifying and verifying signatures or stamps on incoming paperwork in real-time.
Historic Document Classification: Museums, libraries, or archives can use this model to classify their historical documents based on different stamps and signatures. This system could contribute heavily to digital preservation efforts for older or rare documents.
Mail Sorting: Postal services can leverage this model to sort envelopes based on postmark stamps, potentially automating parts of their sorting process and increasing efficiency.

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{
                            idp1_dataset,
                            title = { IDP1 Dataset },
                            type = { Open Source Dataset },
                            author = { Edvard },
                            howpublished = { \url{ https://universe.roboflow.com/edvard-vml7o/idp1 } },
                            url = { https://universe.roboflow.com/edvard-vml7o/idp1 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { sep },
                            note = { visited on 2024-04-26 },
                            }