semvec/pypi-packages huggingface dataset:
TabulaScan 0.2.0
Streamlined Scanned Image Table Extraction and Excel Conversion
Requires Python: >=3.7

# **TabulaScan**

TabulaScan: Streamlined Scanned Image Table Extraction and Excel Conversion

# Project Overview :

TabulaScan is a cutting-edge solution designed to automate the process of table detection, 
recognition, and extraction from scanned images, transforming them into Excel files with remarkable accuracy and efficiency. 
With TabulaScan, you can swiftly transform paper-based tables into structured, editable Excel files, 
enabling seamless integration into your data management processes.

# Key Features :

**Precise Table Identification** : Our algorithm can precisely locate tables within scanned images, even in cases with complex layouts and diverse fonts.

**Robust Image Quality Handling** : It's capable of handling varying image quality levels, ensuring reliable performance across different scanned documents.

**Data Extraction** : Beyond table detection, this algorithm excels at extracting data from these tables, making it a comprehensive tool for data analysis

**Output to Excel** : Convert recognized tables into Excel files, preserving data structure and format.


## Installation

To install TabulaScan, you can use pip:

```bash
pip install TabulaScan
```

## Usage
Hereâ€™s a simple example demonstrating how to use TabulaScan to convert an image of a table into an Excel file and then read it using Pandas:

```bash
import TabulaScan as ts
import cv2
import pandas as pd

# Load your image
img_path = 'path_to_your_image.jpg'
anaylise = cv2.imread(img_path)

# Convert the image table to an Excel table
result = ts.ImgTable2ExcelTable(anaylise)

# Load the resulting Excel file into a Pandas DataFrame
excel_table = pd.read_excel(result)

# Display the extracted table (the table is auto downloaded)
excel_table
```

## Contributing
Contributions are welcome! Please feel free to submit a Pull Request on GitHub.

## Support
If you encounter any issues or have any questions, please feel free to open an issue on the GitHub repository.
cfnresponse 1.1.5
Send a response object to a custom resource by way of an Amazon S3 presigned URL

# cfnresponse

[![PyPi Version](https://badgen.net/pypi/v/cfnresponse)](https://pypi.org/project/cfnresponse/)

This package contains the Amazon Web Services (AWS) cfnresponse module which is
available in Python AWS Lambda environments.

The code for this module can be found [in the AWS documentation](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/cfn-lambda-function-code-cfnresponsemodule.html#w2ab1c23c23c16b9c15)
and in the [awslabs GitHub repo](https://github.com/awslabs/aws-cloudformation-templates/blob/master/aws/services/CloudFormation/MacrosExamples/StackMetrics/lambda/cfnresponse.py)

You can compare the code in `awslabs` with this pypi package with this command

```
if [ "$(curl -s https://raw.githubusercontent.com/awslabs/aws-cloudformation-templates/master/aws/services/CloudFormation/MacrosExamples/StackMetrics/lambda/cfnresponse.py | sha256sum)" = "$(curl -s https://raw.githubusercontent.com/gene1wood/cfnresponse/master/cfnresponse/__init__.py | sha256sum)" ]; then echo "GitHub matches AWS"; else echo "GitHub does not match AWS"; fi
if [ "$(curl -s https://raw.githubusercontent.com/awslabs/aws-cloudformation-templates/master/aws/services/CloudFormation/MacrosExamples/StackMetrics/lambda/cfnresponse.py | sha256sum)" = "$(wget --quiet https://files.pythonhosted.org/packages/f9/b7/76b0abe8003a797ab535cae77e39568092eff18ac0e8e10fa9ffa245e5d3/cfnresponse-1.1.4-py2.py3-none-any.whl && unzip -p cfnresponse-1.1.4-py2.py3-none-any.whl cfnresponse/__init__.py | sha256sum && rm cfnresponse-1.1.4-py2.py3-none-any.whl)" ]; then echo "PyPi matches AWS"; else echo "PyPi does not match AWS"; fi
```

This module [used to use the vendored version of `requests`](https://github.com/awslabs/aws-cloudformation-templates/blob/dd484dd32680fcbfc52b34de45923f78b5626e39/aws/services/CloudFormation/MacrosExamples/StackMetrics/lambda/cfnresponse.py)
provided by `botocore` but this changed in 
[April 2020](https://github.com/awslabs/aws-cloudformation-templates/commit/44b76a1f694f82eeee14fe804bf9dc973fdc2230#diff-f6c57142d56d8704aaf1d429ff1a06a6dd3f2ee6d80f0572ada8af010ff17124)
to instead use `urllib3` as
[`botocore.vendored.requests` was removed](https://github.com/boto/botocore/pull/1829).
sphinx_mdinclude 0.6.2
Markdown extension for Sphinx
Requires Python: >=3.8
Keywords: Markdown, reStructuredText, sphinx-extension

sphinx-mdinclude
================

Sphinx extension for including or writing pages in Markdown format.

[![version](https://img.shields.io/pypi/v/sphinx-mdinclude.svg)](https://pypi.python.org/pypi/sphinx-mdinclude)
[![documentation](https://img.shields.io/badge/docs-latest-success)](https://sphinx-mdinclude.readthedocs.io)
[![changelog](https://img.shields.io/badge/change-log-blue)](https://sphinx-mdinclude.omnilidb.dev/en/latest/changelog.html)
[![license](https://img.shields.io/pypi/l/sphinx-mdinclude.svg)](https://github.com/omnilib/sphinx-mdinclude/blob/main/LICENSE)


sphinx-mdinclude is a simple Sphinx extension that enables including Markdown documents
from within reStructuredText. It provides the `.. mdinclude::` directive, and
automatically converts the content of Markdown documents to reStructuredText format.

sphinx-mdinclude is a fork of [m2r](https://github.com/miyakogi/m2r) and
[m2r2](https://github.com/crossnox/m2r2), focused only on providing a Sphinx extension.

## Features

* Basic markdown and some extensions (see below)
    * inline/block-level raw html
    * fenced-code block
    * tables
    * footnotes (``[^1]``)
* Inline- and Block-level rst markups
    * single- and multi-line directives (`.. directive::`)
    * inline-roles (``:code:`print(1)` ...``)
    * ref-link (``see `ref`_``)
    * footnotes (``[#fn]_``)
    * math extension inspired by [recommonmark](https://recommonmark.readthedocs.io/en/latest/index.html)
* Sphinx extension
    * add markdown support for sphinx
    * ``mdinclude`` directive to include markdown from md or rst files
    * option to parse relative links into ref and doc directives (``md_parse_relative_links``)

## Restrictions

* In the rst's directives, markdown is not available. Please write in rst.
* Column alignment of tables is not supported. (rst does not support this feature)
* Heading with overline-and-underline is not supported.
  * Heading with underline is OK
* Rst heading marks are currently hard-coded and unchangeable.
  * H1: `=`, H2: `-`, H3: `^`, H4: `~`, H5: `"`, H6: `#`

## Installation

Python 3.6 or newer is required.

```
pip install sphinx-mdinclude
```

## Usage

In your Sphinx `conf.py`, add the following lines:

```python
extensions = [
    ...,
    'sphinx_mdinclude',
]
```

Markdown files with the `.md` extension will be loaded and used by Sphinx, similar to
any other `.rst` files.

To include Markdown files within other files, use the `.. mdinclude:: <filename>`
directive. This applies the conversion from Markdown to reStructuredText format.

## License

`sphinx-mdinclude` is copyright Hiroyuki Takagi, CrossNox, and [Amethyst Reese][],
and licensed under the MIT license. I am providing code in this repository to you
under an open source license. This is my personal repository; the license you receive
to my code is from me and not from my employer. See the [LICENSE][] file for details.

[Amethyst Reese]: https://noswap.com
[LICENSE]: https://github.com/omnilib/sphinx-mdinclude/blob/main/LICENSE
convertion 0.0.3
convert all .doc, .docx, .xls, .xlsx, .ppt, and .pptx files to PDF at once

### convertion

convert .doc, .docx, .xls, .xlsx, .ppt, and .pptx files to PDF in the current directory.

#### install it via (pip/pip3):
```
pip install convertion
```

#### enter the conversion menu by (py/python/python3):
```
py -m convertion
```

opt to execute a conversion from the menu; shown as below:
>Please select a conversion:
>1. ALL to pdf
>2. word to pdf
>3. excel to pdf
>4. powerpoint to pdf
>
>Enter your choice (1 to 4): _