Adding Differential Binarization model from PaddleOCR to Keras3

[gowthamkpr]

This adds the Differntial Binarization model for text detection.

Implemented the architecture based on ResNet50_vd from PaddleOCR and ported the weights.

[mattdangerw]

Let's split this up. Start with ResNetVD backbone?

Some notes...

• Remove the aliases. One ResNetVDBackbone can handle all of these with different presets.
• Conversion scripts as scripts not colabs.
• Follow the local style for backbones as closely as possible. See some comments here https://github.com/keras-team/keras-nlp/pull/1737
• Keep models a flat directory. No backbones/xx etc.
• Add some tests.

[divyashreepathihalli]

@gowthamkpr is the PR ready for review?

[divyashreepathihalli]

Hi @gowthamkpr! can you please refactor the code to KerasHub style?

• [ ] Add a preprocessor flow
• [ ] subclass image segementer model for the task class
• [ ] add preset class
• [ ] add standard test routines

[gowthamkpr]

Hi @gowthamkpr! can you please refactor the code to KerasHub style?

I've refactored using SAM as example.

* [ ]  Add a preprocessor flow

I've added DifferentialBinarizationPreprocessor and DifferentialBinarizationImageConverter.

* [ ]  subclass image segementer model for the task class

I've subclassed ImageSegmenter, but I left the custom compile() method, since we need a different loss than the one used in ImageSegmenter's compile().

* [ ]  add preset class

Done. The model is not yet in Kaggle, so I've disabled the presets test for now.

* [ ]  add standard test routines

Done. Not sure if there are additional standard test routines other than the ones used in SAM that should be run.

[sachinprasadhs]

Closing this since the continuation PR is in progress here https://github.com/keras-team/keras-hub/pull/2095