PaddleOCR/ppstructure/layout

Getting Started

[1. Install whl package](#Install whl package)

[2. Quick Start](#Quick Start)

3. PostProcess

4. Results

5. Training

1. Install whl package

wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
pip install -U layoutparser-0.0.0-py3-none-any.whl

2. Quick Start

Use LayoutParser to identify the layout of a given document:

import cv2
import layoutparser as lp
image = cv2.imread("doc/table/layout.jpg")
image = image[..., ::-1]

# load model
model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config", 
                                threshold=0.5,
                                label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
                                enforce_cpu=False, 
                                enable_mkldnn=True)
# detect
layout = model.detect(image)

# show result
show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
show_img.show()

The following figure shows the result, with different colored detection boxes representing different categories and displaying specific categories in the upper left corner of the box with show_element_type

`PaddleDetectionLayoutModel`parameters are described as follows:

parameter	description	default	remark
config_path	model config path	None	Specify config_ path will automatically download the model (only for the first time,the model will exist and will not be downloaded again)
model_path	model path	None	local model path, config_ path and model_ path must be set to one, cannot be none at the same time
threshold	threshold of prediction score	0.5	\
input_shape	picture size of reshape	[3,640,640]	\
batch_size	testing batch size	1	\
label_map	category mapping table	None	Setting config_ path, it can be none, and the label is automatically obtained according to the dataset name_ map
enforce_cpu	whether to use CPU	False	False to use GPU, and True to force the use of CPU
enforce_mkldnn	whether mkldnn acceleration is enabled in CPU prediction	True	\
thread_num	the number of CPU threads	10	\

The following model configurations and label maps are currently supported, which you can use by modifying '--config_path' and '--label_map' to detect different types of content:

dataset	config_path	label_map
TableBank word	lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_word/config	{0:"Table"}
TableBank latex	lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config	{0:"Table"}
PubLayNet	lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config	{0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"}

• TableBank word and TableBank latex are trained on datasets of word documents and latex documents respectively;
• Download TableBank dataset contains both word and latex。

3. PostProcess

Layout parser contains multiple categories, if you only want to get the detection box for a specific category (such as the "Text" category), you can use the following code:

# follow the above code
# filter areas for a specific text type
text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])

# text areas may be detected within the image area, delete these areas
text_blocks = lp.Layout([b for b in text_blocks \
                   if not any(b.is_in(b_fig) for b_fig in figure_blocks)])

# sort text areas and assign ID
h, w = image.shape[:2]

left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)

left_blocks = text_blocks.filter_by(left_interval, center=True)
left_blocks.sort(key = lambda b:b.coordinates[1])

right_blocks = [b for b in text_blocks if b not in left_blocks]
right_blocks.sort(key = lambda b:b.coordinates[1])

# the two lists are merged and the indexes are added in order
text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])

# display result
show_img = lp.draw_box(image, text_blocks,
            box_width=3, 
            show_element_id=True)
show_img.show()

Displays results with only the "Text" category：

4. Results

Dataset	mAP	CPU time cost	GPU time cost
PubLayNet	93.6	1713.7ms	66.6ms
TableBank	96.2	1968.4ms	65.1ms

Envrionment：

​ CPU： Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core

​ GPU： a single NVIDIA Tesla P40

5. Training

The above model is based on PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) ，if you want to train your own layout parser model，please refer to：train_layoutparser_model