3 ms·

Show HN: Pytorch Text Recognition Tool

41 points by s3nh_ 7y ago

abeppu 7y ago
In the example on the readme, why does it reverse the order of "casteli" (3) and "castle" (4)? It's a bit surprising that it understand the rest of the ordering (including the "and" in the center), but flips those two. Also, if I were a developer trying to use this, I'd be constantly annoyed at receiving a dict with keys like "0", "1", "2" rather than just getting a list.
- s3nh_ 7y ago
  In the example, text is not sorted by it's corrdinates but by appearence of boxes in first network. It is visible in more complex documents, that crnn network did not create boxes in descending order (word-by-word). also, good point about the list. dictionary keys has no logical usage in this one.
dpaluy 7y ago
How to train this model in other languages?
- s3nh_ 7y ago
  the hardest part in training model in foreign languages is to get correctly labeled dataset. I worked with pretrain model on Polish language documents and based on this experience it is relatively good if you are using some text similarity measures. There are some examples/pretrain models with Korean/English/French language
boromi 7y ago
And CRAFT stands for what?
- piceas 7y ago
  The topic list gives the answer https://github.com/topics/craft https://github.com/topics/craft Fist repo: Character Region Awareness for Text Detection (CRAFT) https://github.com/clovaai/CRAFT-pytorch https://github.com/clovaai/CRAFT-pytorch Which has a nice video demo. https://youtu.be/HI8MzpY8KMI https://youtu.be/HI8MzpY8KMI
jkaufmann_ 7y ago
This is awesome, definitely a ton of use cases for this. It would be interesting if you put some background into why you made this project in your README. Some inspiration always helps. Also some examples of where else you've seen it applied could spark peoples imagination to help people get some more usage out of your work.
- s3nh_ 7y ago
  Hi, thanks for feedback! I'll add more general information. In my opinion theres a lot to do in complex document classification, I'll try to add some demo to make things more intuitive. thanks!

41 points by s3nh_ 7y ago

In the example on the readme, why does it reverse the order of "casteli" (3) and "castle" (4)? It's a bit surprising that it understand the rest of the ordering (including the "and" in the center), but flips those two. Also, if I were a developer trying to use this, I'd be constantly annoyed at receiving a dict with keys like "0", "1", "2" rather than just getting a list.

s3nh_ 7y ago
In the example, text is not sorted by it's corrdinates but by appearence of boxes in first network. It is visible in more complex documents, that crnn network did not create boxes in descending order (word-by-word). also, good point about the list. dictionary keys has no logical usage in this one.

dpaluy 7y ago

How to train this model in other languages?

s3nh_ 7y ago
the hardest part in training model in foreign languages is to get correctly labeled dataset. I worked with pretrain model on Polish language documents and based on this experience it is relatively good if you are using some text similarity measures. There are some examples/pretrain models with Korean/English/French language

boromi 7y ago

And CRAFT stands for what?

piceas 7y ago
The topic list gives the answer https://github.com/topics/craft https://github.com/topics/craft Fist repo: Character Region Awareness for Text Detection (CRAFT) https://github.com/clovaai/CRAFT-pytorch https://github.com/clovaai/CRAFT-pytorch Which has a nice video demo. https://youtu.be/HI8MzpY8KMI https://youtu.be/HI8MzpY8KMI

jkaufmann_ 7y ago

This is awesome, definitely a ton of use cases for this. It would be interesting if you put some background into why you made this project in your README. Some inspiration always helps. Also some examples of where else you've seen it applied could spark peoples imagination to help people get some more usage out of your work.

s3nh_ 7y ago
Hi, thanks for feedback! I'll add more general information. In my opinion theres a lot to do in complex document classification, I'll try to add some demo to make things more intuitive. thanks!