Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.14 GB | Duration: 3h 6m
Learn and Build Business Card Scanner App from Scratch with Python, Spacy, Pytesseract.
What you’ll 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
Welcome to Course “Automatic Scanned Document Data Extraction OCR NER in Python” !!!
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.
Section -0 : Setting Up Project
Section -1 : Data Preprocessing
Overview on Pytesseract
Extract Text from all Image
Clean and Prepare text
Section – 2: Train Named Entity Recognition Model
Prepare Training Data for Spacy
Section – 3: Prediction
1. Load Model
2. Render and Serve with Displacy
3. Draw Bounding Box on Image
I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks.
With the concepts of image basics, we will then start our project phase-1, face identity recognition. I will start this phase with preprocessing images, we will extract features from the images using deep neural networks. Then with the features of faces, we will train the different Deep learning models like Convolutional Neural Network. I will teach you the model selection and hyperparameter tuning for face recognition models
Once our Deep learning model is ready, will we move to Section-3, and write the code for preforming predictions with CNN model.
Finally, we will develop the desktop application and make prediction to live video streaming.
What are you waiting for? Start the course develop your own Computer Vision Flask Desktop Application Project using Machine Learning, Python and Deploy it in Cloud with your own hands.
Who this course is for:
Anyone who wants to Develop Business Card Reader App
Data Scientist, Analyst, Python Develop who want to enhance skills in NLP