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Getting Started

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 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. If you want to train your own layout parser model，please refer to：train_layoutparser_model

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