Walking into MHacks 6, our team wanted to stretch our knowledge of machine vision while utilizing Capital One's redesigned API fresh off the stack. Interestingly enough, we came across a philipino twitter account that mockingly retweets others' postings with exposed debit card information under the credo: need a debit card?

Now that online banking and transactions are commonplace, we cannot afford to let one mistake compromise a user's account. This technology isn't a consumer-ready application as much as a potential service for financial institutions to help combat fraud. Fraud costs banks heavily in time, money, and personel- not to mention reputation- and that isn't even considering how hard the victim has to stomach it.

Our service simply offers banking instutions preventative measures by beating malicious hackers to the source.

• Pointed at any base url, our python web crawlers and scrapers will retrieve every image url from every linked page nested within the base url.
• Afterwards, image urls are sent to the c++ image processing module that will retrieve any discernable alphanumeric characters from them thanks to a synthesis between openCV and tesseract-ocr.
• Appropriate banking institution will be notified of account's compromised status (i.e. Capital One).
• And more! (I just know the most about how my specific modules operate)