Intelligently Extract Text & Data from Document with OCR NER

It’s time to build a Document Scanner App project that can extract names from scan documents with OpenCV, Pytesseract, and Spacy.

What you’ll learn

Intelligently Extract Text & Data from Document with OCR NER

This means that you will build and train a model for recognizing names.
Extract not only text from the picture, but also other things from the business card.
To make ABBY, you have to start from scratch. It is a business card scanner that you make.
When we solve the Natural Language Problem, we use data processing methods that are very high level.
A real-time app for NER.

Requirements

Should know at least some Python.
With Pandas DataFrames, learn how to do aggregation.
OpenCV lets you read and write images, and you can draw rectangles on them.

Description

This is a course called “Intelligently Extract Text and Data from a Document with OCR and NER.”

In this course, you will learn how to make your own Named Entity Recognizer. The main goal of this class is to learn how to find things like invoices, business cards, shipping bills, Bill of Lading documents, and more from scanned documents like this one. However, for the sake of privacy, we only looked at the Business Card. But you can use the same framework to write any kind of financial report. Following the curriculum below, we will be able to make the project come to fruition soon.

To make this project, we’ll use two of the most important tools in data science:

When it comes to computers, vision is the name of
There are people who work with natural language.

If you want to learn about computer vision, you’ll scan a paper, figure out where the text is, and then get the text from an image. Take out the text’s title, clean it up, and read it. Then we’ll do natural language processing, which is what we’re going to do.

Python libraries that are used in the Computer Vision Module.

OpenCV
Numpy
Pytesseract

Python Libraries used in Natural Language Processing

Spacy
Pandas
Regular Expression
String

As we’re using two major technologies to make the project, we break the course into several stages for easy understanding.

Stage 1: We’ll set up the project by installing things and meeting the rules.

Set up Python.
Make sure you have everything you need.

Stage 2: We will prepare the data. That means we will use Pytesseract to get a text from images and also clean them up.

All of the images that you want to show
a look at Pytesseract
Text can be found in any image that has text on it.
Clean and prepare the text.

Stage 3: We’ll learn how to tag NER data with BIO tagging.

Manually labeling with BIO is how you do it.
B – The beginning
I – Is inside
The outside

Stage 4: We will do even more to clean the text and prepare the data so that we can train machine learning on it.

Spacy training data should be ready before you start
Convert data into a format that is big.
The Named Entity model will be trained at this stage.
Setting up the NER Model
The model should be taught how to do what it should.

Stage 6: We will use NER and a model to predict the titles and build a data pipeline for parsing text.

Take a look at the models
Then, make and serve with a display.
When you take a picture, draw a box around it.
Parse Titles from a text to find them.

Our last step is to make a document scanner app. We will do this by putting everything together.

Let’s start working on the AI project now.

Who this course is for:

Anyone who wants to make an app that reads business cards
Those who work as a data scientist, analyst, or with Python who wants to learn more about NLP