Enhance the OCR recognition accuracy of PPStructure.

[RussellLuo]

This PR is likely to close the following issues:

• PPStructure版面分析得到的结果，bbox里OCR的结果缺失最后一行 #10270
• PPStructure的OCR对layout结果图resize造成像素失真导致识别效果极差 #10322
• paddleOCR识别结果丢失了文档的最后一行 #10367
• PPStructure的识别准确率远低于PaddleOCR #11278
• PaddleOCR对于纯文本的识别比PPStructure更准确 #11665
• PPStructure 这个版面分析没有分析完全，请指教 #11302
• PPStructure得到了版面分析的结果后，对各个块的图进行OCR，有一个放大的预处理，导致像素失真，而OCR对缩放像素失真的鲁棒性很差。 #11328
• 图像Layout检测，一行文字丢失前面部分 #11869

[paddle-bot]

Thanks for your contribution!

[GreatV]

For the picture below, this hot fix didn't work.

INPUT：

OUTPUT ：

[RussellLuo]

In my own opinion, this fix is essentially just a patch (rather than a complete solution), so there may be edge cases where this fix does not address.

UPDATE: That being said, in my own use cases, this patch typically has much higher OCR recognition accuracy when compared to the current implementation of PPStructure.

[RussellLuo]

I took a screenshot of your INPUT image (intentionally leaving extra white space around the last line), and got the OUTPUT as below:

(I guess the omission of the last line of text is due to the original image being cropped.)

[GreatV]

PaddleOCR/ppstructure/predict_system.py

Lines 120 to 149 in c82dd64

	for region in layout_res:
	res = ''
	if region['bbox'] is not None:
	x1, y1, x2, y2 = region['bbox']
	x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
	roi_img = ori_im[y1:y2, x1:x2, :]
	else:
	x1, y1, x2, y2 = 0, 0, w, h
	roi_img = ori_im
	if region['label'] == 'table':
	if self.table_system is not None:
	res, table_time_dict = self.table_system(
	roi_img, return_ocr_result_in_table)
	time_dict['table'] += table_time_dict['table']
	time_dict['table_match'] += table_time_dict['match']
	time_dict['det'] += table_time_dict['det']
	time_dict['rec'] += table_time_dict['rec']
	else:
	if self.text_system is not None:
	if self.recovery:
	wht_im = np.ones(ori_im.shape, dtype=ori_im.dtype)
	wht_im[y1:y2, x1:x2, :] = roi_img
	filter_boxes, filter_rec_res, ocr_time_dict = self.text_system(
	wht_im)
	else:
	filter_boxes, filter_rec_res, ocr_time_dict = self.text_system(
	roi_img)
	time_dict['det'] += ocr_time_dict['det']
	time_dict['rec'] += ocr_time_dict['rec']

Hi @RussellLuo. Could you modify the code here to make it detect and recognize the original image directly, so that the other interfaces don't need to be changed?

[RussellLuo]

I'm not familiar with this piece of code. To my understanding, the core logic is to first detect the layout regions (by using the LayoutPredictor):

PaddleOCR/ppstructure/predict_system.py

Line 114 in c82dd64

layout_res, elapse = self.layout_predictor(img)

and then recognize the corresponding texts from each layout region (by using the TextSystem):

PaddleOCR/ppstructure/predict_system.py

Lines 120 to 125 in c82dd64

	for region in layout_res:
	res = ''
	if region['bbox'] is not None:
	x1, y1, x2, y2 = region['bbox']
	x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
	roi_img = ori_im[y1:y2, x1:x2, :]

As shown above, to get the image pixels roi_img within the layout region, the original image ori_im is cropped according to the coordinates of each layout region. As discussed in #10270, this step is suspected to be the cause of decreased OCR precision, as the coordinates of the layout region are likely to be inaccurate (compared to the coordinates of the text region detected by TextSystem).

Based on the analysis, the core idea behind this fix is to:

1. First detect all possible text regions (by using PaddleOCR, which is essentially a TextSystem).
2. Then detect all layout regions (by using a StructureSystem), and for each layout region:
   • Filter the text regions that intersect with this layout region.
   • For these intersecting text regions, recognize the texts within them (by using PaddleOCR).
   • Finally associate these texts with the this layout region.

Therefore, this fix is a hybrid solution that leverages both PaddleOCR and StructureSystem, but I'm not sure whether it is appropriate to place the changes of this hybrid solution into PaddleOCR/ppstructure/predict_system.py.

Hope to hear your suggestions.

[GreatV]

Based on the analysis, the core idea behind this fix is to:

1. First detect all possible text regions and recognize the texts within them (by only using PaddleOCR, which is essentially a TextSystem).
2. Then collect texts from the text regions that intersect with each layout region (detected by using a StructureSystem).

Yes, I think that would be a more appropriate modification, with minimal impact on the overall code structure. I hope you'll give it a try.

[RussellLuo]

All changes have been centralized in StructureSystem, and here are some additional notes.

The following line would keep results with low confidence and has been deleted:

PaddleOCR/ppstructure/predict_system.py

Line 61 in c82dd64

args.drop_score = 0

These two lines would cause coordinate error and have been commented out. It took me a significant amount of time to identify this issue. I don't understand the logic behind this piece of code, perhaps these lines should be deleted?

PaddleOCR/ppstructure/predict_system.py

Lines 165 to 166 in c82dd64

	if not self.recovery:
	box += [x1, y1]

[GreatV]

Why do we need to keep low confidence objectives?

PaddleOCR/ppstructure/predict_system.py

Line 61 in c82dd64

args.drop_score = 0

The code here is used to calculate the offset from the results of the layout analysis and can be removed.

PaddleOCR/ppstructure/predict_system.py

Lines 165 to 166 in c82dd64

	if not self.recovery:
	box += [x1, y1]

[RussellLuo]

Why do we need to keep low confidence objectives?

This line is from the previous code and will lead to the return of results with low confidence (as the default value 0.5 was reset). The latest change has been made to remove this line.

The code here is used to calculate the offset from the results of the layout analysis and can be removed.

Got it!

[RussellLuo]

All suggested changes have been made and all commits have been squashed into one.

[GreatV]

@RussellLuo Thanks for your contribution, I will take some time to check it again.

[GreatV]

I have run some tests and can confirm that this issue has been improved.

• old:
  

• new:
  

[GreatV]

LGTM

[RussellLuo]

@GreatV Thanks for your patient review and excellent suggestions!

[jzhang533]

thanks, lets merge it.