GitLab

refactor(pdf_extract_kit): optimize image processing and table recognition logicRefactor the image processing logic for OCR and table recognition to ensure
consistency and improve performance. Remove redundant initialization of PIL images,
unify image cropping logic, and streamline the handling of formula detection results.
Also, adjust the table recognition process to improve integration with the updated image
processing logic and enhance overall efficiency.

If 'table-config' is not present in the configuration file, the table recognition
feature will default to being disabled to ensure consistent behavior. This change
adds a warning log and sets a default configuration for table recognition when the
expected config is missing.

* # add table recognition using struct-eqtable
## Changelog
31/07/20204
- Support table recognition. Table images will be converted into html.

### how to use the new feature:
set the attribute 'table-mode' to 'true' in magic-pdf.json

### caution:
it takes 200s to 500s to convert a single table image using cpu

* # add table recognition using struct-eqtable
## Changelog
31/07/20204
- Support table recognition. Table images will be converted into LaTex.

### how to use the new feature:
set the attribute 'table-mode' to 'true' in magic-pdf.json

### caution:
it takes 200s to 500s to convert a single table image using cpu

* # feat(model inference): add table recognition and convertion to LaTeX

# What's Changed

### New Features

- Add table content recognition, we use weights of [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy) to convert table image to LaTex.

### Instruction

- pip install pypandoc struct-eqtable==0.1.0
- Download [StructEqTable weights](https://huggingface.co/wanderkid/PDF-Extract-Kit/tree/main/models/TabRec

) and put it under models/ directory.
- Edit 'table-mode' value to turn on table recognition function which is turned off by default.
- If you did not download any models before, refer to [how to download models](docs/how_to_download_models_zh_cn.md)。

* add table recognition and convertion to LaTeX

* add table recognition and conversion to LaTeX

* add table recognition and conversion to LaTeX

* add table recognition and conversion to LaTeX

---------
Co-authored-by: liukaiwen <liukaiwen@pjlab.org.cn>

## Changelog
31/07/20204
- Support table recognition. Table images will be converted into LaTex.

### how to use the new feature:
set the attribute 'table-mode' to 'true' in magic-pdf.json

### caution:
it takes 200s to 500s to convert a single table image using cpu

## Changelog
31/07/20204
- Support table recognition. Table images will be converted into html.

### how to use the new feature:
set the attribute 'table-mode' to 'true' in magic-pdf.json

### caution:
it takes 200s to 500s to convert a single table image using cpu

fix(pdf_extract_kit): specify utf-8 encoding when reading model configEnsure the model configuration file is read with utf-8 encoding to support
non-ASCII characters and prevent potential encoding errors.

fix(magic-pdf): add default values and improve warning logs for config optionsEnsure that 'temp-output-dir', 'models-dir', and 'device-mode' have sensible default
values in case they are not specified in the config file.

Introduce a Singleton pattern to manage custom models in the magic_pdf module.
This change improves the efficiency by ensuring that a single instance of the
custom model is created and reused, thereby reducing the overhead of multiple
instantiate calls for the same model configuration.

Add new configuration options for custom model directories and device modeselection. This allows users to specify the directory where models are stored
and choose between CPU and GPU modes for model inference. The configurations
are read from a JSON file and can be easily extended to support additional
options in the future.

Introduce a new feature that allows users to choose between a "lite" and a "full"
model mode for PDF document analysis. The "lite" mode uses a faster, less
accurate model, while the "full" mode employs a higher-precision model at the
cost of speed. This selection can be made through the CLI or API, providing
flexibility for different use cases.