Next-level computer vision: When off-the-shelf software options don’t cut it

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Let’s say you’re in Tokyo and you see a billboard with a catchy photo. You want to know what the billboard says, but you can’t read Japanese. No problem! You can use Google Translate to take a picture of the billboard, highlight the text you care about, and then your phone will translate it into English. This is a prime example of computer vision, which allows us to automatically extract, analyze, and understand information from images due to recent advances in machine learning.

Given these advances, we thought it would be straightforward to adapt off-the-shelf computer vision programs to pull important data from identification documents, a task that most fintech companies face in the field. Ultimately we found that off-the-shelf computer vision programs were not sufficiently accurate  - continue reading here.