Mô tả

Welcome to Course "Intelligently Extract Text & Data from Document with OCR NER" !!!

In this course you will learn how to develop customized Named Entity Recognizer. The main idea of this course is to extract entities from the scanned documents like invoice, Business Card, Shipping Bill, Bill of Lading documents etc. However, for the sake of data privacy we restricted our views to Business Card. But you can use the framework explained to all kinds of financial documents. Below given is the curriculum we are following to develop the project.

To develop this project we will use two main technologies in data science are,

  1. Computer Vision

  2. Natural Language Processing

In Computer Vision module, we will scan the document, identify the location of text and finally extract text from the image. Then in Natural language processing, we will extract the entitles from the text and do necessary text cleaning and parse the entities form the text.


Python Libraries used in Computer Vision Module.

  • OpenCV

  • Numpy

  • Pytesseract

Python Libraries used in Natural Language Processing

  • Spacy

  • Pandas

  • Regular Expression

  • String


As are combining two major technologies to develop the project, for the sake of easy to understand we divide the course into several stage of development.

Stage -1: We will setup the project by doing the necessary installations and requirements.

  • Install Python

  • Install Dependencies

Stage -2: We will do data preparation. That is we will extract text from images using Pytesseract and also do necessary cleaning.

  • Gather Images

  • Overview on Pytesseract

  • Extract Text from all Image

  • Clean and Prepare text

Stage -3: We will see how to label NER data using BIO tagging.

  • Manually Labeling with BIO technique

    • B - Beginning

    • I  -  Inside

    • O - Outside

Stage -4: We will further clean the text and preprocess the data for to train machine learning.

  • Prepare Training Data for Spacy

  • Convert data into spacy format

Stage -5: With the preprocess data we will train the Named Entity model.

  • Configuring NER Model

  • Train the model

Stage -6: We will predict the entitles using NER and model and create data pipeline for parsing text.

  • Load Model

  • Render and Serve with Displacy

  • Draw Bounding Box on Image

  • Parse Entitles from Text


Finally, we will put all together and create document scanner app.

Are you ready !!!

Let start developing the Artificial Intelligence project.

Bạn sẽ học được gì

Develop and Train Named Entity Recognition Model

Not only Extract text from the Image but also Extract Entities from Business Card

Develop Business Card Scanner like ABBY from Scratch

High Level Data Preprocess Techniques for Natural Language Problem

Real Time NER apps

Yêu cầu

  • Should be at least beginner in Python
  • Understand aggregation techniques with Pandas DataFrames
  • Read, Write Images with OpenCV and Drawing Rectangles on Image
  • Understand HTML, Boostrap

Nội dung khoá học

11 sections

Introduction

5 lectures
Introduction
03:48
Project Plan
03:09
Project Document
00:00
Download the Resources
00:00
Facing any Issue with the Course ? Here is the solution
06:03

Project Setup

6 lectures
Install Python
02:23
Install Virtual Environment
02:03
Install Packages into Virtual Environment
01:39
Install Tesseract OCR & Pytesseract
05:18
Install spaCy
02:43
Test, the packages are installed
03:17

Data Preparation

10 lectures
Load Business Card using OpenCV & PIL
06:15
Pytesseract: Extract text from Image
03:07
Pytesseract: Tesseract Error
01:24
Pytesseract: How Pytesseract with work ?
03:27
Pytesseract: Image to text to dataframe
08:06
Pytesseract: Clean Text in Dataframe
04:14
Pytesseract: Draw Bounding Box around each word
11:17
Extract Text and Data from all Business Card
12:54
Save data in csv
01:41
Labeling
06:21

Data Preprocessing and Cleaning

8 lectures
Spacy Training Data Format
01:46
Load Data and convert into Pandas DataFrame
05:58
Updated Code.
00:02
Cleaning Text
07:47
Convert Data into spacy format
06:33
Testing Entities
01:23
Convert data into spacy format for all Business card text
02:48
Splitting Data into Training and Testing Set
03:10

Train Named Entity Recognition (NER) model

4 lectures
Spacy: Fill the Configuration
06:54
Spacy: Prepare Data
08:32
Spacy: Train NER pipeline model
01:39
Spacy: Save NER Model
00:56

Predictions

18 lectures
Import Required Libraries
02:07
Clean Text Function
00:58
Load Spacy NER Model
01:51
Extract Text from Image and Convert into Data Frame
04:58
Convert Data Frame into Content
02:16
Get Named Entities from model
02:37
Displacy render
00:46
Tagging Each Word
05:29
Join Label to tokens dataframe
04:05
Join token dataframe with Pytesseract data
10:03
Bounding Box and Tagging Predicted Entities
07:00
Combine the BIO information
06:36
Bounding Box
10:59
Parsing Function
07:28
Testing
01:55
Parse Entitles
10:44
Predictions Function
07:04
Final Prediction Pipeline
06:43

Improve Model Performance

4 lectures
Ideas to Improve model accuracy
01:59
Version-2 model framework: Data Preprocessing
05:58
Train Version 2 model
04:14
Get Predictions from the model
03:24

Document Scanner

11 lectures
Download the Resources
00:00
What and Why Document Scanner in OpenCV ?
01:04
Setup and Read Image
03:31
Resize Image with same aspect ratio
04:22
Edge Detection (Enhance, Blur and Canny) to Document
06:08
Dilate Edges with morphological transform
03:36
Find Four Point Countours (Identify Location of document)
05:36
Apply Wrap transform and crop only document
04:21
Document Scanner Function: Putting All together
08:17
Magic Color to Image
11:16
Integrate NER Predictions
06:32

Document Scanner Web App

21 lectures
What will you Develop ?
02:15
Download Web App
00:01
Setting Up Web App Project
07:07
Install VS Code
02:33
Install Flask
00:36
First Flask App
04:27
Run HTML file with Flask server
02:29
Our Web App design steps
02:25
Step-1: Design Page: Create Navigation Bar in HTML
09:47
Step-1: Create About Page
05:53
Step-2: Create HTML form to Upload Image or File in HTML
04:55
Step-3: How to Predict document coordinates with Python in Flask
06:07
Step-2: Upload and save image Backend : create settings.py
03:00
Step-2: Upload and save image Backend: save image from HTML form
09:11
Step-3: Document Scanning
16:18
Adjust coordinates of document using JavaScript
20:51
Wrap and Crop the document and save the image
16:13
Get Predictions
08:49
Design Predictions page
03:58
Display results in table
04:43
Final
02:00

Appendix

1 lectures
Limitations of Pytesseract
03:26

BONUS

1 lectures
Bonus Lecture: Next Steps
00:14

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