Table Extraction Tool

Overview

Tree Structure - Table Extraction

Fonduer has been successfully extended to perform information extraction from richly formatted data such as tables. A crucial step in this process is the construction of the hierarchical tree of context objects such as text blocks, figures, tables, etc. The system currently uses PDF to HTML conversion provided by Adobe Acrobat converter. Adobe Acrobat converter is not an open source tool and this can be very inconvenient for Fonduer users. We therefore need to build our own module as replacement to Adobe Acrobat. Several open source tools are available for pdf to html conversion but these tools do not preserve the cell structure in a table. Our goal in this project is to develop a tool that extracts text, figures and tables in a pdf document and maintains the structure of the document using a tree data structure.

This project is using the table-extraction tool (https://github.com/xiao-cheng/table-extraction).

Dependencies

pip install -r requirements.txt

Environment variables

First, set environment variables. The DATAPATH folder should contain the pdf files that need to be processed.

source set_env.sh

Tutorial

The table-extraction/tutorials/ folder contains a notebook table-extraction-demo.ipynb. In this demo we detail the different steps of the table extraction tool and display some examples of table detection results for paleo papers. However, to extract tables for new documents, the user should directly use the command line tool detailed in the next section.

Command Line Usage

To use the tool via command line, run:

source set_env.sh

python table-extraction/ml/extract_tables.py [-h]

usage: extract_tables.py [-h] [--mode MODE] [--train-pdf TRAIN_PDF]
                         [--test-pdf TEST_PDF] [--gt-train GT_TRAIN]
                         [--gt-test GT_TEST] [--model-path MODEL_PATH]
                         [--iou-thresh IOU_THRESH]

Script to extract tables bounding boxes from PDF files using a machine
learning approach. if model.pkl is saved in the model-path, the pickled model
will be used for prediction. Otherwise the model will be retrained. If --mode
is test (by default), the script will create a .bbox file containing the
tables for the pdf documents listed in the file --test-pdf. If --mode is dev,
the script will also extract ground truth labels fot the test data and compute
some statistics. To run the script on new documents, specify the path to the
list of pdf to analyze using the argument --test-pdf. Those files must be
saved in the DATAPATH folder.

optional arguments:
  -h, --help            show this help message and exit
  --mode MODE           usage mode dev or test, default is test
  --train-pdf TRAIN_PDF
                        list of pdf file names used for training. Those files
                        must be saved in the DATAPATH folder (cf set_env.sh)
                        must be saved in the DATAPATH folder (cf set_env.sh)
  --test-pdf TEST_PDF   list of pdf file names used for testing. Those files
                        must be saved in the DATAPATH folder (cf set_env.sh)
  --gt-train GT_TRAIN   ground truth train tables
  --gt-test GT_TEST     ground truth test tables
  --model-path MODEL_PATH
                        pretrained model
  --iou-thresh IOU_THRESH
                        intersection over union threshold to remove duplicate
                        tables

Each document must be saved in the DATAPATH folder.

The script will create a .bbox file where each row contains tables coordinates of the corresponding row document in the --test_pdf file.

The bounding boxes are stored in the format (page_num, page_width, page_height, top, left, bottom, right) and are separated with ";".

Evaluation

We provide an evaluation code to compute recall, precision and F1 score at the character level.

python table-extraction/evaluation/char_level_evaluation.py [-h] pdf_files extracted_bbox gt_bbox

usage: char_level_evaluation.py [-h] pdf_files extracted_bbox gt_bbox

Computes scores for the table localization task. Returns Recall and Precision
for the sub-objects level (characters in text). If DISPLAY=TRUE, display GT in
Red and extracted bboxes in Blue

positional arguments:
  pdf_files       list of paths of PDF file to process
  extracted_bbox  extracting bounding boxes (one line per pdf file)
  gt_bbox         ground truth bounding boxes (one line per pdf file)

optional arguments:
  -h, --help      show this help message and exit
Owner
HazyResearch
We are a CS research group led by Prof. Chris Ré.
HazyResearch
TextBoxes++: A Single-Shot Oriented Scene Text Detector

TextBoxes++: A Single-Shot Oriented Scene Text Detector Introduction This is an application for scene text detection (TextBoxes++) and recognition (CR

Minghui Liao 930 Jan 04, 2023
Regions sanitàries (RS), Sectors Sanitàris (SS) i Àrees Bàsiques de Salut (ABS) de Catalunya

Regions sanitàries (RS), Sectors Sanitaris (SS), Àrees de Gestió Assistencial (AGA) i Àrees Bàsiques de Salut (ABS) de Catalunya Fitxers GeoJSON de le

Glòria Macià Muñoz 2 Jan 23, 2022
This pyhton script converts a pdf to Image then using tesseract as OCR engine converts Image to Text

Script_Convertir_PDF_IMG_TXT Este script de pyhton convierte un pdf en Imagen luego utilizando tesseract como motor OCR convierte la Imagen a Texto. p

alebogado 1 Jan 27, 2022
Layout Analysis Evaluator for the ICDAR 2017 competition on Layout Analysis for Challenging Medieval Manuscripts

LayoutAnalysisEvaluator Layout Analysis Evaluator for: ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records ICD

17 Dec 08, 2022
Text page dewarping using a "cubic sheet" model

page_dewarp Page dewarping and thresholding using a "cubic sheet" model - see full writeup at https://mzucker.github.io/2016/08/15/page-dewarping.html

Matt Zucker 1.2k Dec 29, 2022
Awesome Spectral Indices in Python.

Awesome Spectral Indices in Python: Numpy | Pandas | GeoPandas | Xarray | Earth Engine | Planetary Computer | Dask GitHub: https://github.com/davemlz/

David Montero Loaiza 98 Jan 02, 2023
An expandable and scalable OCR pipeline

Overview Nidaba is the central controller for the entire OGL OCR pipeline. It oversees and automates the process of converting raw images into citable

81 Jan 04, 2023
Convert scans of handwritten notes to beautiful, compact PDFs

Convert scans of handwritten notes to beautiful, compact PDFs

Matt Zucker 4.8k Jan 01, 2023
Programa que viabiliza a OCR (Optical Character Reading - leitura óptica de caracteres) de um PDF.

Este programa tem o intuito de ser um modificador de arquivos PDF. Os arquivos PDFs podem ser 3: PDFs verdadeiros - em que podem ser selecionados o ti

Daniel Soares Saldanha 2 Oct 11, 2021
Perspective recovery of text using transformed ellipses

unproject_text Perspective recovery of text using transformed ellipses. See full writeup at https://mzucker.github.io/2016/10/11/unprojecting-text-wit

Matt Zucker 111 Nov 13, 2022
learn how to use Gesture Control to change the volume of a computer

Volume-Control-using-gesture In this project we are going to learn how to use Gesture Control to change the volume of a computer. We first look into h

Diwas Pandey 49 Sep 22, 2022
A Python wrapper for Google Tesseract

Python Tesseract Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded i

Matthias A Lee 4.6k Jan 06, 2023
Creating a virtual tv using opencv in python3.

Virtual-TV Creating a virtual tv using opencv in python3. In order to run the code follow the below given steps: Make sure the desired videos which ar

Vamsi 1 Jan 01, 2022
InverseRenderNet: Learning single image inverse rendering, CVPR 2019.

InverseRenderNet: Learning single image inverse rendering !! Check out our new work InverseRenderNet++ paper and code, which improves the inverse rend

Ye Yu 141 Dec 20, 2022
Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. This Neural Network (NN) model recognizes the text contained in the images of segmented words.

Handwritten-Text-Recognition Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. T

27 Jan 08, 2023
POT : Python Optimal Transport

This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

Python Optimal Transport 1.7k Jan 04, 2023
Balabobapy - Using artificial intelligence algorithms to continue the text

Balabobapy - Using artificial intelligence algorithms to continue the text

qxtony 1 Feb 04, 2022
EAST for ICPR MTWI 2018 Challenge II (Text detection of network images)

EAST_ICPR2018: EAST for ICPR MTWI 2018 Challenge II (Text detection of network images) Introduction This is a repository forked from argman/EAST for t

QichaoWu 49 Dec 24, 2022
Morphological edge detection or object's boundary detection using erosion and dialation in OpenCV python

Morphologycal-edge-detection-using-erosion-and-dialation the task is to detect object boundary using erosion or dialation . Here, use the kernel or st

Tamzid hasan 3 Nov 25, 2022
Ackermann Line Follower Robot Simulation.

Ackermann Line Follower Robot This is a simulation of a line follower robot that works with steering control based on Stanley: The Robot That Won the

Lucas Mazzetto 2 Apr 16, 2022