Intelligently Extract Text & Data from Document with OCR NER

Intelligently Extract Text & Data from Document with OCR NER, available at $69.99, has an average rating of 4.65, with 92 lectures, based on 374 reviews, and has 3408 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to Develop Business Card Reader App or Data Scientist, Analyst, Python Develop who want to enhance skills in NLP It is particularly useful for Anyone who wants to Develop Business Card Reader App or Data Scientist, Analyst, Python Develop who want to enhance skills in NLP.

Enroll now: Intelligently Extract Text & Data from Document with OCR NER

Summary

Title: Intelligently Extract Text & Data from Document with OCR NER

Price: $69.99

Average Rating: 4.65

Number of Lectures: 92

Number of Published Lectures: 89

Number of Curriculum Items: 92

Number of Published Curriculum Objects: 89

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Anyone who wants to Develop Business Card Reader App
Data Scientist, Analyst, Python Develop who want to enhance skills in NLP

Target Audiences

Anyone who wants to Develop Business Card Reader App
Data Scientist, Analyst, Python Develop who want to enhance skills in NLP

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,

Computer Vision
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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Project Plan

Lecture 3: Project Document

Lecture 4: Download the Resources

Lecture 5: Facing any Issue with the Course ? Here is the solution

Chapter 2: Project Setup

Lecture 1: Install Python

Lecture 2: Install Virtual Environment

Lecture 3: Install Packages into Virtual Environment

Lecture 4: Install Tesseract OCR & Pytesseract

Lecture 5: Install spaCy

Lecture 6: Test, the packages are installed

Chapter 3: Data Preparation

Lecture 1: Load Business Card using OpenCV & PIL

Lecture 2: Pytesseract: Extract text from Image

Lecture 3: Pytesseract: Tesseract Error

Lecture 4: Pytesseract: How Pytesseract with work ?

Lecture 5: Pytesseract: Image to text to dataframe

Lecture 6: Pytesseract: Clean Text in Dataframe

Lecture 7: Pytesseract: Draw Bounding Box around each word

Lecture 8: Extract Text and Data from all Business Card

Lecture 9: Save data in csv

Lecture 10: Labeling

Chapter 4: Data Preprocessing and Cleaning

Lecture 1: Spacy Training Data Format

Lecture 2: Load Data and convert into Pandas DataFrame

Lecture 3: Updated Code.

Lecture 4: Cleaning Text

Lecture 5: Convert Data into spacy format

Lecture 6: Testing Entities

Lecture 7: Convert data into spacy format for all Business card text

Lecture 8: Splitting Data into Training and Testing Set

Chapter 5: Train Named Entity Recognition (NER) model

Lecture 1: Spacy: Fill the Configuration

Lecture 2: Spacy: Prepare Data

Lecture 3: Spacy: Train NER pipeline model

Lecture 4: Spacy: Save NER Model

Chapter 6: Predictions

Lecture 1: Import Required Libraries

Lecture 2: Clean Text Function

Lecture 3: Load Spacy NER Model

Lecture 4: Extract Text from Image and Convert into Data Frame

Lecture 5: Convert Data Frame into Content

Lecture 6: Get Named Entities from model

Lecture 7: Displacy render

Lecture 8: Tagging Each Word

Lecture 9: Join Label to tokens dataframe

Lecture 10: Join token dataframe with Pytesseract data

Lecture 11: Bounding Box and Tagging Predicted Entities

Lecture 12: Combine the BIO information

Lecture 13: Bounding Box

Lecture 14: Parsing Function

Lecture 15: Testing

Lecture 16: Parse Entitles

Lecture 17: Predictions Function

Lecture 18: Final Prediction Pipeline

Chapter 7: Improve Model Performance

Lecture 1: Ideas to Improve model accuracy

Lecture 2: Version-2 model framework: Data Preprocessing

Lecture 3: Train Version 2 model

Lecture 4: Get Predictions from the model

Chapter 8: Document Scanner

Lecture 1: Download the Resources

Lecture 2: What and Why Document Scanner in OpenCV ?

Lecture 3: Setup and Read Image

Lecture 4: Resize Image with same aspect ratio

Lecture 5: Edge Detection (Enhance, Blur and Canny) to Document

Lecture 6: Dilate Edges with morphological transform

Lecture 7: Find Four Point Countours (Identify Location of document)

Lecture 8: Apply Wrap transform and crop only document

Lecture 9: Document Scanner Function: Putting All together

Lecture 10: Magic Color to Image

Lecture 11: Integrate NER Predictions

Chapter 9: Document Scanner Web App

Lecture 1: What will you Develop ?

Lecture 2: Download Web App

Lecture 3: Setting Up Web App Project

Lecture 4: Install VS Code

Lecture 5: Install Flask

Lecture 6: First Flask App

Lecture 7: Run HTML file with Flask server

Lecture 8: Our Web App design steps

Lecture 9: Step-1: Design Page: Create Navigation Bar in HTML

Lecture 10: Step-1: Create About Page

Lecture 11: Step-2: Create HTML form to Upload Image or File in HTML

Lecture 12: Step-3: How to Predict document coordinates with Python in Flask

Lecture 13: Step-2: Upload and save image Backend : create settings.py

Lecture 14: Step-2: Upload and save image Backend: save image from HTML form

Lecture 15: Step-3: Document Scanning

Lecture 16: Adjust coordinates of document using JavaScript

Lecture 17: Wrap and Crop the document and save the image

Lecture 18: Get Predictions

Lecture 19: Design Predictions page

Lecture 20: Display results in table

Lecture 21: Final

Chapter 10: Appendix

Lecture 1: Limitations of Pytesseract

Chapter 11: BONUS

Lecture 1: Bonus Lecture: Next Steps

Instructors

G Sudheer
Instructor
datascience Anywhere
Team of Engineers
Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 2 votes
2 stars: 5 votes
3 stars: 32 votes
4 stars: 115 votes
5 stars: 220 votes

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