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A unified toolkit for Deep Learning Based Document Image Analysis

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We provide a series of examples for to help you start using the layout parser library:

1.[Table OCR and Results Parsing](https://github.com/Layout-Parser/layout-parser/blob/master/examples/OCR%20Tables%20and%20Parse%20the%20Output.ipynb):`layoutparser` can be used for conveniently OCR documents and convert the output in to structured data.

1.[Table OCR and Results Parsing](https://github.com/Layout-Parser/layout-parser/blob/main/examples/OCR%20Tables%20and%20Parse%20the%20Output.ipynb):`layoutparser` can be used for conveniently OCR documents and convert the output in to structured data.

2.[Deep Layout Parsing Example](https://github.com/Layout-Parser/layout-parser/blob/master/examples/Deep%20Layout%20Parsing.ipynb): With the help of Deep Learning,`layoutparser` supports the analysis very complex documents and processing of the hierarchical structure in the layouts.

2.[Deep Layout Parsing Example](https://github.com/Layout-Parser/layout-parser/blob/main/examples/Deep%20Layout%20Parsing.ipynb): With the help of Deep Learning,`layoutparser` supports the analysis very complex documents and processing of the hierarchical structure in the layouts.

We could also check how the trained layout model performs on the input

image. Following this

`instruction<https://github.com/Layout-Parser/layout-parser/blob/master/examples/Deep%20Layout%20Parsing.ipynb>`__,

`instruction<https://github.com/Layout-Parser/layout-parser/blob/main/examples/Deep%20Layout%20Parsing.ipynb>`__,

we could conveniently load a layout prediction model and run predictions

on the existing image.