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Matrix Data Extractor:
Tabular data extraction from PDF documents is critical task due to diverse PDF templates and Table templates. Some open-source tools do not support all possible types of PDF templates for tabular data extraction. A computer vision based document table detection approach is considered along with Camelot tool to extract tabular information from PDF documents. A post-processing work is necessary after tabular data extraction.
Type of tool: Web application to be deployed on your computer
Short description of the tool: Extract tabular data and textual data from product technical datasheets (PDF documents)
Matrix Data Extractor (MDE) is a web-based application that identifies document table regions on PDF documents using Computer Vision based Deep Learning algorithm and extracts data to text files by applying Optical Character Recognition (OCR). It supports to transfer extracted data to MongoDB database tables. A search functionality is also provided to retrieve data on user interface based on Keyword matching (e.g. Manufacturer Name, Technical Datasheet Name, Keyword for Table Data).
Required skills:
- Elementary User: No programming
- Advanced User: Python, Basic Deep Learning (PyTorch)
Required programs (step-by-step guide and links provided in user guideline blow):
- Python
- Java
- Anaconda
- Tool files from the GitHub (link below)
Disclaimer:
Any support to provide table detection model will not be provided unfortunately after project completion. The accuracy of table detection model depends on various factors such as volume, variety of annotated datasets, hyperparameters of model. You can do your experiment to get better accuracy of your table detection model.
Screenshots:
This tool supports you to:
- (Text)
Example use case:
- (Text)
Tool guideline and access:
- ⚠️ We recommend to open and save the user guideline before proceeding: Data Extractor/MatrixDataExtractor_UserGuide.pdf
- The tool can be accessed throughout the following link: https://share.streamlit.io/cslab-hub/data_validation/main/main.py
- Get the code/installation files from github https://cslab-hub-data-validation-main-bx6ggw.streamlitapp.com/ and start using the app by browsing through the pages.
Get the GitHub https://github.com/cslab-hub/MatrixDataExtractor, copy
the code into your computer, prepare your annotated dataset and start using it
Getting Started#
The code for the tool is available at https://github.com/cslab-hub/MatrixDataExtractor
Table Detection : Annotated Datasets, Model Weights, Model Inference#
Table detection model weights and datasets can be provided on request. It is not publicly available. Also a Jupyter Notebook can be provided on request to show model inference result on domain specific dataset.Secret Key for 'backend' Django Web Application:#
Please use Secret Key as 'SECRET_KEY=!zhn#9$0pvr!+jp5q0f-vhvkfp0w$@tpvy4kf20pb89vf#w1q-' in mde.env file without single quotes.
Contact person of the tool:
Arnab Ghosh Chowdhury, mailto:arnab.ghosh.chowdhury@uni-osnabrueck.de form the Osnabrueck University.
Before applying this tool:
We recommend also taking a look at the following Di-Plast tools below. They can help you to gather necessary information and data, help to better prepare your data and continue working with it afterwards:
--> Improve internal information and material flow -> VSM
--> Get guidance to set up a working data infrastructure -> Data Infrastructure Wiki
--> Find the right sensor to survey your process -> Sensor Tool
-->Analyse and Visualize your process data with data analytics -> Data Analytics
-->Get important insights in enhancing your data understanding
-> Exploratory Pattern Analytics
After applying this tool:
-->Match material requirements with material properties -> Matrix
