Review US driver's license images and label personal, document, and visual fields for document understanding models. This entry-level, worldwide contractor role offers flexible part-time work and $1 to $7 per hour.
Image & Video Annotation
100% Remote Hourly · $1–$7/hr
$1–$7/hr
Compensation
Worldwide
Eligibility
Entry
Experience
Mar 24, 2026
Posted
Open worldwide
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About AI Training and Image Annotation
AI training is the human work behind modern artificial intelligence. People prepare and review examples so models can recognize images, understand documents, extract information, and produce more useful results.
In image annotation, contributors label visual content according to detailed instructions. Your work on driver's license images will help improve document understanding and information extraction models.
Remote work that can fit around studies, another job, or family responsibilities
An entry-level way to gain experience contributing to AI systems
Flexible part-time work requiring less than 20 hours per week
The Role
As a US Driver's License Image Annotation Specialist, you will review images of US driver's licenses and accurately label personal, document, and visual information across various US driver's license templates crowdsourced from the internet.
This is a part-time contractor position with less than 20 hours per week. The listed pay is $5 per hour, with an hourly range of $1 to $7.
Employment type: Contractor and part time
Experience level: Entry level
Work location: Worldwide and remote
Data type: Images
Annotation format: Bounding boxes
Labeling software: Label Studio
Pay: $1 to $7 per hour, with $5 per hour listed
What You'll Do
You will examine each driver's license image, identify the relevant regions, and label the specified fields accurately. Careful reading and consistent field recognition are essential because the dataset is intended to train document understanding and information extraction models.
Label jurisdiction_name, credential_type, real_id_indicator, family_name, given_name, middle_name, and suffix
Label date_of_birth, sex, height, weight, eye_color, hair_color, street_address, city, state, and zip_code
Label document_number, class, endorsements, restrictions, date_of_issue, and date_of_expiration
Label under_18_until, under_21_until, organ_donor_indicator, veteran_indicator, and customer_id_number
Identify and label visual elements including the portrait, ghost_image, and signature
Use bounding-box annotation in Label Studio to mark the relevant information on each image
Requirements
No specific education, work experience, or professional background is required. Previous experience with data labeling, document annotation, or image tagging is helpful but not required.
You should be comfortable reading English text on identification documents and recognizing common US formats for addresses, dates, and document fields.
Basic familiarity with the appearance of a standard US driver's license or state ID
Ability to clearly read English text in license images
Ability to recognize common US address formats, dates, and document fields
Strong attention to detail when extracting information from ID images
Previous annotation or image-tagging experience is a plus, not a requirement
Why This Work Matters
Every major AI system depends on carefully prepared and reviewed examples. By labeling driver's license fields consistently, you will contribute to the development of models that can interpret complex documents and extract structured information from images.
The work offers a practical entry point into a fast-growing technology field, with flexible hours and no requirement for a specialized educational background.
How to Apply Through OpenTrain
Create a free OpenTrain account, build your profile, and apply in minutes. If selected, you can complete this remote contractor work on a flexible part-time schedule of less than 20 hours per week.
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Complete your profile with your relevant skills and experience
Apply for the US Driver's License Image Annotation Specialist role
Review project instructions and begin labeling in Label Studio if selected
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