This is a state-of-the-art DeepLayout Analysis implementation based on Tensorflow to accurately detect, qualify, extract and recognize/OCR every field from a bank credit card using a single image: Number, Holder’s name, Validity, Company…
Our implementation works with all cards (credit, debit, travel, prepaid, corporate…) from all payment networks (Visa, MasterCard, American Express, RuPay, Discover…).
Both Embossed and UnEmbossed formats are supported.
Unlike other implementations we’re not doing brute force OCR (trying multiple images/parameters until match). You only need a single image to get the correct result. There is no template matching which means the data could be malformed or at any position and you’ll still have the correct result (WYSIWYG).
You can reach 100% precision on the credit card number recognition using data validation process.
The number of use cases in FinTech industry are countless: Scan To Pay, Helping visually impaired users, Online shopping speed-up, payment forms auto-filling, better user experience, reducing typing errors, process automation...
Don’t take our word for it, come check our implementation. No registration, license key or internet connection is required, just clone the code from Github and start coding/testing. Everything runs on the device, no data is leaving your computer. The code released on Github comes with many ready-to-use samples to help you get started easily.
You can also check our online cloud-based implementation (no registration required) to check out the accuracy and precision before starting to play with the SDK.
Cloud-based implementation: https://www.doubango.org/webapps/credit-card-ocr/
Open source Computer Vision library: https://github.com/DoubangoTelecom/compv
- C++ API
- Supported formats
- Architecture overview
- Configuration options
- Sample applications
- Getting started
- Data validation
- Rectification layer
- Muti-threading design
- Memory management design
- Improving the accuracy
- Improving the speed
- Best JSON config
- Debugging the SDK
- Frequently Asked Questions (FAQ)