Today, we’re excited to announce a new Mechanical Turk case study from LinkedIn, the world’s largest professional online network. The new case study details how LinkedIn is able to manually transcribe tens of thousands of business cards each day to support CardMunch, an iOS application that turns business cards into LinkedIn connections and digital contacts.
CardMunch’s developers integrated with Amazon MechanicalTurk in order to achieve the transcription accuracy required for a great customer experience. They considered using off-the-shelf optical character recognition (OCR) solutions, but the OCR didn’t provide accurate enough results – every character needs to be transcribed correctly in order for the information to be useful to CardMunch users. With Amazon Mechanical Turk, the team was able to achieve near-absolute accuracy using human transcribers from the Mechanical Turk marketplace.
Millions of cards have been transcribed using Amazon Mechanical Turk since the launch of the CardMunch app. “With the Amazon Mechanical Turk marketplace, we manually transcribe tens of thousands of cards each day, while having the ability to burst up and scale down as needed,” says Sid Viswanathan, the developer and product manager of CardMunch. “We’ve been able to drive down operational costs while not having to guess at our capacity needs.”
To learn more about how LinkedIn is able to utilize Mechanical Turk to scale digital transcription, and explore other use cases from customers, such as Acxiom, AOL, and DARPA, see all of our case studies online on the Mechanical Turk Requester site.
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