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https://github.com/the-black-knight-01/Tabulo

Table Detection and Extraction Using Deep Learning ( It is built in Python, using Luminoth, TensorFlow<2.0 and Sonnet.)
https://github.com/the-black-knight-01/Tabulo

deep-learning detection faster-r-cnn luminoth ocr pdf-table-extraction python sonnet ssd table-data-extraction table-detection table-detection-using-deep-learning table-recognition tabulo tensorflow tesseract

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Table Detection and Extraction Using Deep Learning ( It is built in Python, using Luminoth, TensorFlow<2.0 and Sonnet.)

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README 

        
[![Tabulo](https://github.com/interviewBubble/Tabulo/raw/master/docs/images/Tabulo_logo.png)](https://github.com/interviewBubble/Tabulo)



---



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 [Luminoth](https://github.com/tryolabs/luminoth), [TensorFlow](https://www.tensorflow.org/) and [Sonnet](https://github.com/deepmind/sonnet).



### Table of Contents

1. **[Installation Instructions](#1-installation-instructions)**


2. **[Avaiable API's](#2-avaiable-apis)**


3. **[Working with pretrained Models](#3-working-with-pretrained-models)**


4. **[Runnning Tabulo](#4-runnning-tabulo)**


5. **[Runnning Tabulo As Service](#5-runnning-tabulo-as-service)**


6. **[Supported models ](#6-supported-models)**


7. **[Usage](#7-usage)**


8. **[Working with datasets](#8-working-with-datasets)**


9. **[Training](#9-training)**


10. **[LICENSE](#10-license)**


## 1. Installation Instructions

Tabulo currently supports Python 2.7 and 3.4–3.6. 



### 1.1 Pre-requisites



To use Tabulo, [TensorFlow](https://www.tensorflow.org/install/) 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`.



We are using tesseract to extract data from table so you have to install tesseract also. [Follow this link to install tessersact](https://interviewbubble.com/install-tesseract-on-mac-linux-windows-centos/)



### 1.2 Installing Tabulo



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



```bash

git clone https://github.com/interviewBubble/Tabulo.git

cd tabulo

pip 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. Working with pretrained Models:

* DOWNLOAD pretrained model from [Google drive](https://drive.google.com/drive/folders/1aUh9RfGn2XGgG2EtpKFh7P6PmcC3Q48z?usp=sharing)

* 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:

![Checkpoints](https://github.com/interviewBubble/Tabulo/raw/master/docs/images/Checkpoints.png)

* Now run server using this command: ```tabulo server web --checkpoint 6aac7a1e8a8e```



## 4. Runnning Tabulo



### 4.1 Running Tabulo as Web Server:

![Running Tabulo](https://github.com/interviewBubble/Tabulo/blob/master/docs/images/tabulo_server.png)



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

![Example of Table Detection with Faster R-CNN By Tabulo](https://github.com/interviewBubble/Tabulo/blob/master/docs/images/table_detect.png)



### 4.3 Example of Table Data Extraction with tesseract By Tabulo:

![Example of Table Data Extraction with tesseract By Tabulo](https://github.com/interviewBubble/Tabulo/blob/master/docs/images/table_data_extract.png)



## 5. Runnning Tabulo As Service:



### 5.1 Using Curl command

```Curl command to detect tabel

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

``` 

### 5.2 With PostMan

#### Header Section:

![Table Detection using Postman](https://github.com/interviewBubble/Tabulo/blob/master/docs/images/tabulo_resquest_header.png)



#### Data Section:

![Table Detection using Postman](https://github.com/interviewBubble/Tabulo/raw/master/docs/images/table_detect_API.png)



## 6. Supported models



Currently, we support the following models:



* **Object Detection**

  * [Faster R-CNN](https://arxiv.org/abs/1506.01497)

  * [SSD](https://arxiv.org/abs/1512.02325)



We also provide **pre-trained checkpoints** for the above models trained on popular datasets such as [COCO](http://cocodataset.org/) and [Pascal](http://host.robots.ox.ac.uk/pascal/VOC/).



## 7. 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  --help`



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



## 8. Working with datasets



 [DataSet to train your custom model](https://github.com/interviewBubble/Table-Detection-using-Deep-Learning/tree/master/data).



## 9. Training



See [Training your own model](https://github.com/interviewBubble/Table-Detection-using-Deep-Learning) to learn how to train locally or in Google Cloud.



## 10. LICENSE

Released under the [BSD 3-Clause](LICENSE).



--------------

# References

* https://github.com/Sargunan/Table-Detection-using-Deep-learning

* https://github.com/tryolabs/luminoth