Lyft, Inc.

Aahel Iyer

Joan Devassy

Nadha Nafeeza Gafoor

Ritwik Subir Das

Wei You

John Michael Sparks

Swarn Avinash Kumar

Wiebke Poerschke

Hristo Stefanov Stefanov

Kia Nishimine

Angela Lam

This disclosure describes a card-scan system that can update a card-scan machine learning model to improve card-character predictions for payment cards, driver licenses, or other character-bearing cards by using an active-learning technique that learns from card-scan representations indicating corrections by users to predicted card characters. In particular, the disclosed systems can use a client device to capture and analyze a set of card images of a character-bearing card to predict card characters using a card-scan machine learning model. The disclosed systems can further receive card-scan gradients representing one or more corrections to incorrectly predicted card characters. Based on the card-scan gradients, the disclosed systems can generate active-learning metrics and retrain or update the card-scan machine learning model based on such active-learning metrics. By utilizing and updating the card-scan machine learning model, the disclosed systems can improve the accuracy with which card-character-detection systems predict card characters while preserving data security and verifying the presence of a physical character-bearing card.

Automatically updating a card-scan machine learning model based on predicting card characters

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San Francisco, CA, United States of America

Aahel Iyer