Comments (2)
Hi,
The word "min" in the name refers to the size of the model file (we remove all data related to the training state and leave only the model weights).
We have no plans to release new models in the short term, as our hardware is limited and is currently used in other projects. Nonetheless, you can download several public available datasets (labeled at different levels) and train your own models. For instance, in our examples folder you can find the links to OHG, Bozen and cBAD datasets.
Regards,
from p2pala.
Thanks for reply! I found a good trick in P2PaLA to improve accuracy of binding each segment.
Initially the usual algorythm works returning a polygon/
Then I turn the image on 180 and pass to the program again. It returns a new set of polygons but they are based on "overline" of the lines. Then all the coordinates of polygons of turned images are turned back and can be merged with the first set. The first set works fine with bottom of lines, the second one is good in upper border.
But anyway - the big problem of top sides of capital letters still remains. Almost all capital letters are cropped on their top.
If you can suggest any idea -please share. I see the only way, it's labeled dataset by "overlines" instead of baseline - there all capitla letters should be covered properly.
from p2pala.
Related Issues (20)
- [config] HOT 1
- evaluation HOT 1
- Different predictions when changing the images order in prod_img_list HOT 1
- Error when run pretrained model HOT 1
- JoseRPrietoF version? HOT 2
- TextLine region HOT 4
- XML generator HOT 5
- [Enhancement] Page-XML extractor HOT 2
- Weights initialisation error
- minor typo in command HOT 1
- error while running the pre trained model in google colab HOT 3
- Baseline + polygon detection of handwriting HOT 4
- Demo not working HOT 1
- Wights used on the Website demo HOT 1
- "Neural Networks based Model" link in docs not working HOT 1
- How to see output polygon drawn on an input image? HOT 4
- Does P2Pala support reading order? HOT 1
- model for zone segmentation HOT 1
- require opencv-python-headless variant HOT 2
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from p2pala.