Passport Data Prediction

## Overview

Passport Data Prediction is a dataset for object detection tasks - it contains Name annotations for 686 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Passport photos dataset

This dataset contains over 100,000 passport photos from 100+ countries, making it a valuable resource for researchers and developers working on computer vision tasks related to passport verification, biometric identification, and document analysis. This dataset allows researchers and developers to train and evaluate their models without the ethical and legal concerns associated with using real passport data. By leveraging this dataset, developers can… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/synthetic-passports.

Synthetic US Passports (Hard)

This dataset is designed to evaluate VLMs transcription capabilities by using a well-known and straightforward document type: passports. More specifically, it requires VLMs to be robust to:

tilted documents high-resolution image with a small region of interest (since the passport only takes up a part of the image) HARD VERSION ONLY: noise injected using the Augraphy package, leading to a much more difficult transcription

Note: there is a "sister"… See the full description on the dataset page: https://huggingface.co/datasets/arnaudstiegler/synthetic_us_passports_hard.

This set contains the passport of the Great Kyz Kala monument from 2018 in PDF format and 11 images.

Optical images of printed circuit boards as well as detailed annotations of any text, logos, and surface-mount devices (SMDs). There are several hundred samples spanning a wide variety of manufacturing locations, sizes, node technology, applications, and more. pcb_image: Optical images of each PCB surface and rear, tagged with a unique identifier. color_checker: Pallette to account for environmental illumination factors as well as a scale reference for the photo resolution. Each pcb image indicates which color checker it is associated with. ocr_annotation: Optical Character Recognition annotations. This includes polygon boundaries around all relevant text on a PCB image. Whether the piece of text is on the board or a device, whether it is a logo or not, orientation, and more are noted within the columns of the csv. smd_annotation: Surface-mount Device (SMD) annotations. This includes polygon boundaries around all relevant SMD devices such as resistors, capacitors, inductors, transistors, diodes, LEDs, and more. Along with each component, its associated silkscreen designator ('L', 'R', 'C', 'U', etc.) is recorded. vtp_annotation: Vias, traces, and pins (VTP) annotations. These are regions of connectivity between SMDs on a PCB. Few annotations currently exist, this is considered in 'beta' mode currently. metadata: Holds two files corresponding to information about image files. pcb.csv holds information about the physical PCB samples such as their color, online item description, and any notes. color_checker.csv indicates the pixels per millimeter (ppmm) of any image associated with that color checker, whether an X-Rite ColorChecker Passport or Nano was used, what camera performed the acquisition, and any relevant notes. Each annotation file is designed to be compatible with the S3A application (https://gitlab.com/ficsresearch/s3a or https://pypi.org/project/s3a/), a Python tool for visualizing polygon annotations on an image.

Performance data for individual study participants. (XLSX)

OnlyIDsPPI

## Overview

OnlyIDsPPI is a dataset for object detection tasks - it contains Signature Passport Plates annotations for 8,620 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Summary Data. (XLSX 32 kb)