---
license: mit
metrics:
- accuracy 99.51%
tags:
- OrientationDetection
- TextOrientation
---

Updated CNN .keras model with input layer 256*256 RGB, and 1 output node ranging from 0 (no rotation) to 1 (180 degrees). 

Its accuracy on the dataset in the link below is 99.51%, and the AUC (Area Under Curve) metric is 0.9990. 
https://drive.google.com/file/d/1WENDw-Ggwy32KIdMlMmuF6NcO0gH3mC8/view?usp=sharing

It only recognizes 0 vs 180 degrees as there's another robust way to distinguish +- 90 from 0 and 180 degrees (another model under UTSC DSU). 
Additionally, the dataset only contains upright images because the training program generates the 180-degrees counterparts on the fly. 
As you will see, the model is only trained on text slices resulted from my proprietary preprocessing methods 
(old version already on GitHub https://github.com/digitalutsc/ml_image_tool, but I will update it soon), 
So directly feeding the page images to the model may not work well. 

I used MobileNetV2 with imagenet weights as the backbone, and the accuracy proves the method to be very successful.

![hist_outputs](https://cdn-uploads.huggingface.co/production/uploads/67fc828a84224b6e5a42d981/uQWaG8Nsq5Xx6IcatFzEm.png)