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Pass the `lang` parameter to `custom_model_init` in `doc_analyze` to support language-specific OCR configurations. This enhancement allows the use of language information to improve OCR accuracy when processing PDFs.

* feat(cli&analyze&pipeline): add start_page and end_page args for paginationAdd start_page_id and end_page_id arguments to various components of the PDF parsing
pipeline to support pagination functionality. This feature allows users to specify the
range of pages to be processed, enhancing the efficiency and flexibility of the system.

* feat(cli&analyze&pipeline): add start_page and end_page args for paginationAdd start_page_id and end_page_id arguments to various components of the PDF parsing
pipeline to support pagination functionality. This feature allows users to specify the
range of pages to be processed, enhancing the efficiency and flexibility of the system.

* feat(cli&analyze&pipeline): add start_page and end_page args for paginationAdd start_page_id and end_page_id arguments to various components of the PDF parsing
pipeline to support pagination functionality. This feature allows users to specify the
range of pages to be processed, enhancing the efficiency and flexibility of the system.

Previously, images were not enlarged if their width or height exceeded 3000 pixels.
This threshold has been increased to 9000 pixels to better handle high-resolutionscans and improve the analysis of documents with larger dimensions.

* # 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 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

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.

1.Disable scaling when loading large images.
2.Moving the logic for channel conversion in image processing.