Hello author, after reading charnet paper and code, I have some questions:
1. Character Branch
In 3.2. Character Branch of paper, it said:
This branch contains three sub-branches, for text instance segmentation, character detection and character recognition, respectively.
But in the model.py, I didn't find the Text instance segmentation sub-branch as depicted in Figure 2. In your code, it is replaced by a shrunk char region score prediction branch just like EAST model?
Below is some visualizion sample using your pretrained model:
(I used cv2.applyColorMap(), cv2.addWeighted() and cv2.polylines() for better visualization)
(the angle output is None???)
So, charnet's Character Branch is in fact a EAST-like head(shrunk char score map & geometry map) + char recognition head ?
2. ic15 testset performance
I used the pretrained model and the default config file, the result on ic15 testset is:
precision:0.966 recall:0.744 hmean:0.841
which is far away from the paper report, I noticed that the pred_char_orient in CharDetector class is None. So these open-sourced code is incompleted ?
3. Iterative Character Detection
Iterative Character Detection method is the key for charnet-training in real-world datasets. During each step(2nd~4th step), the parameter of Model A which generates pseudo-gt char-bboxes is fixed, and is different from the Model B to be trained ? or there is only one Model during the whole train schedule?
Looking forward to your reply, thanks!
Hello author, after reading charnet paper and code, I have some questions:
1. Character Branch
In 3.2. Character Branch of paper, it said:
But in the model.py, I didn't find the Text instance segmentation sub-branch as depicted in Figure 2. In your code, it is replaced by a shrunk char region score prediction branch just like EAST model?
Below is some visualizion sample using your pretrained model:
(I used cv2.applyColorMap(), cv2.addWeighted() and cv2.polylines() for better visualization)
(the angle output is None???)
So, charnet's Character Branch is in fact a EAST-like head(shrunk char score map & geometry map) + char recognition head ?
2. ic15 testset performance
I used the pretrained model and the default config file, the result on ic15 testset is:
which is far away from the paper report, I noticed that the
pred_char_orientin CharDetector class is None. So these open-sourced code is incompleted ?3. Iterative Character Detection
Iterative Character Detection method is the key for charnet-training in real-world datasets. During each step(2nd~4th step), the parameter of Model A which generates pseudo-gt char-bboxes is fixed, and is different from the Model B to be trained ? or there is only one Model during the whole train schedule? Looking forward to your reply, thanks!