On 13 May 2025, at 11:38, Ravi Kakadiya <ravikak...@gmail.com> wrote:Dear Team,
I hope this message finds you well.
I am currently working on extracting data from a document using the KYC feature. However, I’ve noticed a discrepancy between the output received from the website(https://www.doubango.org/webapps/kyc-documents-verif) you provided and the output from the application(verify).
Please find the attached screenshot for your reference.
Could you kindly assist in clarifying why there is a difference between the two outputs, and advise on how to proceed?
Looking forward to your support.
Best regards,
Ravi Kakadiya--
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<verify_output.txt><document.jpg><website_output.png>
Dear Team,
Thank you for your response and the detailed technical explanation.
I wanted to share that I’ve tried multiple times with the same image, and in my case, it consistently works—even when I compress the resolution. The results have been stable across different runs on my system.
I understand that the process involves randomness and may behave differently across environments, but based on my tests, the output has been reliable so far.
Please let me know if there’s any specific configuration or test case you’d like me to try on my end to help further investigate this.
Best regards,
Ravi Kakadiya
may be you have a point here:
- your logs show that the document is classified as being "Australia - Medicare Card #1 (Interim)" instead of "Germany - Id Card (2010-2021) Side B"
- when I try on my machine (Windows 8)
using verify app, I got the same
issue
- on the same machine, when I change --vino_activation "auto"
to "--vino_activation "off" it works fine
- looks like an issue with OpenVINO. The
server doesn't use OpenVINO, it uses a GPU (CUDA/TensorRT)
let me check why it works with Tensorflow
and TensorRT/CUDA and doesn't work with OpenVINO
attached 2 results using your image: they're different. Tried right now on using the online demo. Your claim that "it consistently works" or "reliable so far" is not correct.
there is no "specific configuration" to try. I spent time explaining in details so that you understand you must not cut the image and if you do so it's at your own risk.
To view this discussion visit https://groups.google.com/d/msgid/doubango-ai/b666ae44-3bea-4fa9-a720-ce4b45fa00fd%40doubango.org.
I have checked the issue with OpenVINO and the problem is that it detects 2 cards. Check the attached image (blue and red boxes).
The OpenVINO model is generated from the Tensorflow one but is optimized. If you check your card you can understand why it could be confused as being 2.
I have done a quality check on OpenVINO running it against a dataset of 8,000 images and the accuracy is what we expect. This particular image produces different result with openVino but there is no issue in the model optimization.
We're working to replace Tensorflow and OpenVINO with ONNX-RT in all our projects.
For now I don't have a fix for this particular image. To get same result as the online demo you'll have to use a GPU or disable OpenVINO to fallback to Tensorflow on CPU. Check https://www.doubango.org/SDKs/kyc-documents-verif/docs/Configuration_options.html#openvino-activation
To view this discussion visit https://groups.google.com/d/msgid/doubango-ai/56344946-bd0b-41e1-9107-bccd6b3fb45c%40doubango.org.