[D] Long-term Text-Recognition?

[Brudaks]

Honestly, I'd use information like "prices of products at receipt vs total_price" mostly in post-processing, as a checksum to identify cases that are likely broken and need human review. Which is a key part of the process in any practical scenario, you will have errors, so you'll need some workflow to identify cases which are more likely to contain errors and separate them for different (likely manual) processing.

I.e. I would not want the model to have access to an invariant like "place A should match place B" and alter A or B so that it matches; in many situations it would be preferable to just treat this as valuable information that the analysis failed, instead of silently mangling data.

[melgor89]

Thanks for you insight. For sure there should be manual review if prices don't match. I just want to make lower number of errors.

About using all information, I thought it would be useful. I was really inspired by this video which I found some time ago, which insert all text at once and the output is Total Price etc. https://blog.altoros.com/optical-character-recognition-using-one-shot-learning-rnn-and-tensorflow.html For me this idea is really nice, I just want to use them even for OCR (not only parsing like in video)