Intelligently Extract Text & Data from Document with OCR NER. Develop Document Scanner App project that is Named entity extraction from scan documents with OpenCV, Pytesseract, Spacy
- 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
Intelligently Extract Text & Data from Document with OCR NER Course Requirements
- Should be at least beginner in Python
- Understand aggregation techniques with Pandas DataFrames
- Read, Write Images with OpenCV and Drawing Rectangles on Image
Intelligently Extract Text & Data from Document with OCR NER Course Description
Welcome to Course “Intelligently Extract Text & Data from Document with OCR NER” !!!
In this course, you will learn how to set up an accountant. The main idea of this process is to remove entities from scanned documents such as invoices, business cards, shipping invoices, bill of lading files, etc. However, in order to protect personal information, we have restricted the visibility of the business card. However, you can use the framework described in all financial statements. Below is the training material that we follow to develop the project. To design this project, we will be using two key pieces of research data.
- computer visual knowledge
- natural language processing
The Computer Vision module scans the data, identifies the location of the text, and finally removes the text from the image. It then removes the rules from the natural language text, makes the text appropriate, and identifies the places that make up the text.
Python library used by Computer Vision Module.
OpenCV
numpy
the phytesseract
Python library is used to create natural languages
- spatial
- Panda
- regular instruction
- string of characters
As we combine two key technologies to create a project, we break the process down into several stages of development for ease of understanding.
Step 1: Configure the project by performing the necessary installation and requirements. install python
Improve success
Step 2: Prepare your information. Having said that, I am using Pytesseract to remove text from the image while doing the necessary cleanup.
image search
Presentation of Pytesseract
Remove tags from all images
Prepare and prepare documents
- Step 3: Find out how to save NER files using BIO tagging.
The registry uses BIO technology
B – start
I – inside
oh – outside
Step 4: Clean up additional and pre-existing data to teach machine learning.
Prepare training material for the Center
Convert files to a spatial format
Step 5: Identify the product name using the predefined file. Configuring the NER model
model training
Step 6: Use the NER and the model to estimate eligibility and generate baseline data for identification.
load structure
Rendering and moving assistance
Check the box next to the image
Analyze the need from the text
Finally, put it all together to create a file scanning app.
Are you ready !!!
Let’s start creating a smart project.
This course For:
Those who want to create a business card reader app
Data Scholar, Analyst, and Python Designer wishing to improve their NLP skills
Joining Link: https://www.udemy.com/course/business-card-reader-app/