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Table Detection Using Deep Learning, Object Detection, Faster R-CNN


Tabulo is an open-source toolkit for computer vision. Currently, we support table detection, but we are aiming for much more. It is built in Python, using LuminothTensorFlow and Sonnet.

Table of Contents

  1. Installation Instructions
  2. Avaiable API’s
  3. Runnning Tabulo
  4. Runnning Tabulo As Service
  5. Supported models 
  6. Usage
  7. Working with pretrained Models
  8. Working with datasets
  9. Training

1. Installation Instructions

Tabulo currently supports Python 2.7 and 3.4–3.6.

1.1 Pre-requisites

To use Tabulo, TensorFlow must be installed beforehand. If you want GPU support, you should install the GPU version of TensorFlow with pip install tensorflow-gpu, or else you can use the CPU version using pip install tensorflow.

1.2 Installing Tabulo

First, clone the repo on your machine and then install with pip:

git clone https://github.com/interviewBubble/Tabulo.gitcd tabulopip install -e .

1.3 Check that the installation worked

Simply run tabulo --help.

2. Avaiable API’s

  • localhost:5000/api/fasterrcnn/predict/ – To detect table in the image
  • localhost:5000/api/fasterrcnn/extract/ – Extract table content from detected tables

3. Runnning Tabulo

3.1 Running Tabulo as Web Server:

Running Tabulo

3.2 Example of Table Detection with Faster R-CNN By Tabulo:

Example of Table Detection with Faster R-CNN By Tabulo

3.3 Example of Table Data Extraction with tesseract By Tabulo:

Example of Table Data Extraction with tesseract By Tabulo

4. Runnning Tabulo As Service:

4.1 Using Curl command

curl -X POST   http://localhost:5000/api/fasterrcnn/predict/   -H 'Content-Type: application/x-www-form-urlencoded'   -H 'Postman-Token: 70478bd2-e1e8-442f-b0bf-ea5ecf7bf4d8'   -H 'cache-control: no-cache'   -H 'content-type: multipart/form-data; boundary=----WebKitFormBoundary7MA4YWxkTrZu0gW'   -F image=@/path/to/image/page_8-min.jpg

4.2 With PostMan

Table Detection using Postman

5. Supported models

Currently, we support the following models:

We also provide pre-trained checkpoints for the above models trained on popular datasets such as COCO and Pascal.

6. Usage

There is one main command line interface which you can use with the tabulo command. Whenever you are confused on how you are supposed to do something just type:

tabulo --help or tabulo <subcommand> --help

and a list of available options with descriptions will show up.

7. Working with pretrained Models:

  • DOWNLOAD pretrained model from Google drive
  • Unzip and Copy downloaded luminoth folder inside luminoth/utils/pretrained_models folder
  • Hit this command to list all check points: tabulo checkpoint list
  • You will get output like this: 
  • Now run server using this command: tabulo sever web --checkpoint 6aac7a1e8a8e

8. Working with datasets

DataSet to train your custom model.

9. Training

See Training your own model to learn how to train locally or in Google Cloud.


Released under the BSD 3-Clause.


List of Good Articles & PDFs for Object Detection:

1. Object detection: speed and accuracy comparison (Faster R-CNN, R-FCN, SSD, FPN, RetinaNet and YOLOv3)

LINK: https://medium.com/@jonathan_hui/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo-5425656ae359

2. Building a Production Grade Object Detection System with SKIL and YOLO

LINK: https://blog.skymind.ai/building-a-production-grade-object-detection-system-with-skil-and-yolo/

3. mAP (mean Average Precision) for Object Detection

LINK: https://medium.com/@jonathan_hui/map-mean-average-precision-for-object-detection-45c121a31173

4. Object detection: speed and accuracy comparison (Faster R-CNN, R-FCN, SSD and YOLO)

LINK: https://mc.ai/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo/

5. Image Classification Architectures review

LINK: https://medium.com/@14prakash/image-classification-architectures-review-d8b95075998f

6. Zero to Hero: Guide to Object Detection using Deep Learning: Faster R-CNN, YOLO, SSD

LINK: https://cv-tricks.com/object-detection/faster-r-cnn-yolo-ssd/

7. R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms

LINK: https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e

8. Comparative Study of Object Detection Algorithms

LINK: https://www.irjet.net/archives/V4/i11/IRJET-V4I11103.pdf

git how to fetch all remote branches

You can fetch all branches from all remotes like this:

git fetch --all

It’s basically a power move.

fetch updates local copies of remote branches so this is always safe for your local branches BUT:

  1. fetch will not update local branches (which track remote branches); if you want to update your local branches you still need to pull every branch.
  2. fetch will not create local branches (which track remote branches), you have to do this manually. If you want to list all remote branches: git branch -a

To update local branches which track remote branches:

git pull --all

However, this can be still insufficient. It will work only for your local branches which track remote branches. To track all remote branches execute this oneliner BEFORE git pull --all:

git branch -r | grep -v '->' | while read remote; do git branch --track "${remote#origin/}" "$remote"; done

TL;DR version

git branch -r | grep -v '->' | while read remote; do git branch --track "${remote#origin/}" "$remote"; donegit fetch --allgit pull --all

(It seems that pull fetches all branches from all remotes, but I always fetch first just to be sure.)

Run the first command only if there are remote branches on the server that aren’t tracked by your local branches.

P.S. AFAIK git fetch --all and git remote update are equivalent.

Best Time to Visit Pondicherry

Although Pondicherry has a warm climate, the time from October to March can be considered an ideal time to visit. However, the city has different attractions in different seasons. Here is a monthly breakup of Pondicherry’s climatic conditions so you can plan to go:

October to February: These months constitute the winter season in Pondicherry starting from October and February. It is during this time when the climate is comparatively cooler and also perfect for sightseeing, beach fun and water sports. There should be a sufficient amount of light during winter, the temperature usually does not go below 17 ° C here.

March to June: Pondicherry is at its hottest during the summer months beginning from March to June, with maximum temperatures typically not exceeding 41 ° C. However, the crowd is less, making it a good time to enjoy a peaceful holiday in this small town. Even water activities can be enjoyed as the number of herds on the beach is small.

July to September: These months constitute the monsoon season in Pondicherry. The city receives light rainfall every year. Those who like to get wet in the rain; This is the best time to cut your hair and experience the lush green landscape of Pondicherry. Also, during the month of August is a good time to visit the city as Sri Aurobindo’s birthday celebrations are held when the city is at its best.

ERROR: The Python zlib extension was not compiled. Missing the zlib?

Use this command:

CFLAGS="-I$(brew --prefix readline)/include -I$(brew --prefix openssl)/include -I$(xcrun --show-sdk-path)/usr/include" LDFLAGS="-L$(brew --prefix readline)/lib -L$(brew --prefix openssl)/lib" PYTHON_CONFIGURE_OPTS=--enable-unicode=ucs2 pyenv install -v 2.7.0

Stack trace:

running build_scripts

running install_lib

changing mode of /Users/admin/.pyenv/versions/2.7.0/lib/python2.7/lib-dynload/_Drag.so to 755

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Installed Python-2.7 to /Users/admin/.pyenv/versions/2.7.0

Convert pdf to jpg in python

The pdf2image library can be used.

Step 1:

First you need poppler-utils

pdftoppm and pdftocairo are the piece of software that do the actual magic. It is distributed as part of a greater package called poppler.

Using pip

Windows users will have to install poppler for Windows, then add the bin/ folder to PATH.

Mac users will have to install poppler for Mac.

Linux users will have both tools pre-installed with Ubuntu 16.04+ and Archlinux. If it’s not, run 

sudo apt install poppler-utils

Using conda

conda install -c conda-forge poppler

Then you can install the pip package!

pip install pdf2image

Install Pillow if you don’t have it already with 

pip install pillow


Step 2:

Once installed you can use following code to get images.

from pdf2image import convert_from_pathpages = convert_from_path('pdf_file', 500)


Step 3

Saving pages in jpeg format

i = 0
for page in pages:

i += 1 page.save('out'+str(i)+'.jpg', 'JPEG')

Table Detection using Deep Learning

Step 1: Preprocessing Tables
Step 1: Preprocessing Tables

We are using Luminoth(Tensorflow) as backend. Luminoth only work on preprocessed(greyscale) images.  

Use below script to convert images into grayscale images:


import osimport cv2import pandas as pdroot_dir = os.getcwd()file_list = ['train.csv', 'val.csv']image_source_dir = os.path.join(root_dir, 'data/images/')data_root = os.path.join(root_dir, 'data')for file in file_list:    image_target_dir = os.path.join(data_root, file.split(".")[0])    if not os.path.exists(image_target_dir):        os.mkdir(image_target_dir)    # read list of image files to process from file    image_list = pd.read_csv(os.path.join(data_root, file), header=None)[0]    print("Start preprocessing images")    for image in image_list:        # open image file        img = cv2.imread(os.path.join(image_source_dir, image))        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)        # perform transformations on image        b = cv2.distanceTransform(img, distanceType=cv2.DIST_L2, maskSize=5)        g = cv2.distanceTransform(img, distanceType=cv2.DIST_L1, maskSize=5)        r = cv2.distanceTransform(img, distanceType=cv2.DIST_C, maskSize=5)        # merge the transformed channels back to an image        transformed_image = cv2.merge((b, g, r))        target_file = os.path.join(image_target_dir, image)        print("Writing target file {}".format(target_file))        cv2.imwrite(target_file, transformed_image)print("Finished preprocessing images")

Spep 2: Create TFRecords
Spep 2: Create TFRecords

To train our model we need convert our preprocessed images into TFrecords.

run this script. create TFRecords

lumi dataset transform --type csv --data-dir data/ --output-dir tfdata/ --split train --split val --only-classes=table

Step 3: Training our Model
Step 3: Training our Model
Start training the luminoth network
Run this script: start_traning.sh

lumi train -c config.yml


train:  # Name used to identify the run. Data inside `job_dir` will be stored under  # `run_name`.  run_name: table-area-detection-0.1  # Base directory in which model checkpoints & summaries (for Tensorboard) will  # be saved.  job_dir: jobs/  save_checkpoint_secs: 10  save_summaries_secs: 10  # Number of epochs (complete dataset batches) to run.  num_epochs: 10dataset:  type: object_detection  # From which directory to read the dataset.  dir: tfdata/classes-table/  image_preprocessing:    min_size: 600    max_size: 1024  data_augmentation:    - flip:        left_right: True        up_down: True        prob: 0.5model:  type: fasterrcnn  network:    # Total number of classes to predict.    num_classes: 1


It can take more than 1 day(24 hours) to train.

You will see last output like this:

INFO:tensorflow:Saving checkpoints for 3371 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3371, file: b'9527_018.png', train_loss: 2.469784736633301, in 23.64s
INFO:tensorflow:Saving checkpoints for 3372 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3372, file: b'9526_017.png', train_loss: 2.6023592948913574, in 21.86s
INFO:tensorflow:Saving checkpoints for 3373 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3373, file: b'1742_157.png', train_loss: 2.5478856563568115, in 22.36s
INFO:tensorflow:Saving checkpoints for 3374 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3374, file: b'9528_061.png', train_loss: 3.118919849395752, in 21.22s
INFO:tensorflow:Saving checkpoints for 3375 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3375, file: b'9516_001.png', train_loss: 3.0582146644592285, in 21.71s
INFO:tensorflow:Saving checkpoints for 3376 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3376, file: b'9509_040.png', train_loss: 2.7756423950195312, in 22.40s
INFO:tensorflow:Saving checkpoints for 3377 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3377, file: b'9508_065.png', train_loss: 3.152759552001953, in 22.57s
INFO:tensorflow:Saving checkpoints for 3378 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3378, file: b'5008_029.png', train_loss: 2.618196725845337, in 21.45s
INFO:tensorflow:Saving checkpoints for 3379 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3379, file: b'9518_018.png', train_loss: 2.6546759605407715, in 21.48s
INFO:tensorflow:Saving checkpoints for 3380 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3380, file: b'9515_024.png', train_loss: 3.292630434036255, in 21.28s
INFO:tensorflow:finished training after 10 epoch limit

To minimize training loss, you can train 20 epochs also.

Step 4: store last checkpoint
Step 4: store last checkpoint

you have to store last checkpoint. it will be used in the prediction step.


lumi checkpoint create config.yml

Step 5: Prediction
Step 5: Prediction

You can use luminoth in two form:

  1. command line
  2. luminoth web server
  3. Using python

1. Command line:

Run this script on cmd:

lumi predict --checkpoint c2155084dca6 data/val/9541_023.png

Run this script to start Luminoth web server:

lumi server web --checkpoint c2155084dca6

3. Using Python:

Follow this Link:


Kaggle Winning Solutions Github

UPDATED: Oct 17, 2019


Elo Merchant Category Recommendation


Santander Customer Transaction Prediction

PetFinder.my Adoption Prediction

Santander Product Recommendation

Text Classification

Jigsaw Unintended Bias in Toxicity Classification

Quora Insincere Questions Classification

Quora Question Pairs

Toxic Comment Classification Challenge

Document Classification & Data extraction

Tradeshift Text Classification

Time series analysis

Two Sigma: Using News to Predict Stock Movements

Web Traffic Time Series Forecasting

Rossmann Store Sales

Recommendation System:

Santander Product Recommendation

Coreference Resolution

Gendered Pronoun Resolution

Signal Processing

LANL Earthquake Prediction

Image Classification

Cdiscount’s Image Classification Challenge

Right Whale Recognition

Video Challenge

The 3rd YouTube-8M Video Understanding Challenge

Semantic Segmentation & Instance Segmentation

APTOS 2019 Blindness Detection

iMaterialist (Fashion) 2019 at FGVC6:

TGS Salt Identification Challenge

Airbus Ship Detection Challenge

2018 Data Science Bowl (DSB2018)


Generative Dog Images

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%matplotlib inline

If you want to add plots to your Jupyter notebook, then %matplotlib inline is a standard solution. And there are other magic commands will use matplotlib interactively within Jupyter.

%matplotlib: any plt plot command will now cause a figure window to open, and further commands can be run to update the plot. Some changes will not draw automatically, to force an update, use plt.draw()

%matplotlib notebook: will lead to interactive plots embedded within the notebook, you can zoom and resize the figure

%matplotlib inline: only draw static images in the notebook

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