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.
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.
Stage -3: We will see how to label NER data using BIO tagging.
Stage -4: We will further clean the text and preprocess the data for to train machine learning.
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.
Finally, we will put all together and create document scanner app.
Are you ready !!!
Let start developing the Artificial Intelligence project.
