Udemy - Automatic Scanned Document Data Extraction OCR NER in Python

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  • TypeTutorials
  • LanguageEnglish
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  • Last checkedNov. 13th '21
  • Date uploadedNov. 11th '21
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Infohash : 028A44AB6A0AEF7F5BBD5A82165A27E4E602BFDD

Automatic Scanned Document Data Extraction OCR NER in Python



https://TutGee.com

Genre: eLearning | MP4 | Video: h264, 1280x720 | 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

Description
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

Files:

[ TutGee.com ] Udemy - Automatic Scanned Document Data Extraction OCR NER in Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Project Introduction & Plan.mp4 (11.7 MB)
    • 1. Project Introduction & Plan.srt (4.2 KB)
    2. Project Setup
    • 1. Install Python.mp4 (16.9 MB)
    • 1. Install Python.srt (2.8 KB)
    • 2. Install Virtual Environment.mp4 (7.9 MB)
    • 2. Install Virtual Environment.srt (2.7 KB)
    • 3. Install Packages into Virtual Environment.mp4 (7.3 MB)
    • 3. Install Packages into Virtual Environment.srt (1.8 KB)
    • 4. Install Tesseract OCR & Pytesseract.mp4 (40.7 MB)
    • 4. Install Tesseract OCR & Pytesseract.srt (6.3 KB)
    • 5. Install spaCy.mp4 (28.0 MB)
    • 5. Install spaCy.srt (3.1 KB)
    • 6. Test, the packages are installed.mp4 (9.1 MB)
    • 6. Test, the packages are installed.srt (3.8 KB)
    3. Data Preprocessing
    • 1. Load Business Card using OpenCV & PIL.mp4 (32.1 MB)
    • 1. Load Business Card using OpenCV & PIL.srt (7.9 KB)
    • 10. Labeling.mp4 (42.7 MB)
    • 10. Labeling.srt (8.2 KB)
    • 2. Pytesseract Extract text from Image.mp4 (17.4 MB)
    • 2. Pytesseract Extract text from Image.srt (4.1 KB)
    • 3. Pytesseract Tesseract Error.mp4 (12.8 MB)
    • 3. Pytesseract Tesseract Error.srt (1.7 KB)
    • 4. Pytesseract How it will Work .mp4 (65.0 MB)
    • 4. Pytesseract How it will Work .srt (10.4 KB)
    • 5. Pytesseract Image to text to dataframe.mp4 (14.8 MB)
    • 5. Pytesseract Image to text to dataframe.srt (4.6 KB)
    • 6. Pytesseract Clean Text in Dataframe.mp4 (27.2 MB)
    • 6. Pytesseract Clean Text in Dataframe.srt (5.1 KB)
    • 7. Pytesseract Draw Bounding Box around each word.mp4 (72.4 MB)
    • 7. Pytesseract Draw Bounding Box around each word.srt (13.1 KB)
    • 8. Extract Text and Data from all Business Card.mp4 (80.8 MB)
    • 8. Extract Text and Data from all Business Card.srt (15.1 KB)
    • 9. Save data in csv.mp4 (9.5 MB)
    • 9. Save data in csv.srt (2.0 KB)
    4. Training Named Entity Model (NER)
    • 1. Spacy Training Data Format.mp4 (10.7 MB)
    • 1. Spacy Training Data Format.srt (2.0 KB)
    • 10. Spacy Train NER pipeline model.mp4 (5.6 MB)
    • 10. Spacy Train NER pipeline model.srt (2.1 KB)
    • 11. Spacy Save NER Model.mp4 (4.4 MB)
    • 11. Spacy Save NER Model.srt (1.3 KB)
    • 2. Load Data and convert into Pandas DataFrame.mp4 (36.0 MB)
    • 2. Load Data and convert into Pandas DataFrame.srt (7.4 KB)
    • 3. Cleaning Text.mp4 (35.1 MB)
    • 3. Cleaning Text.srt (9.7 KB)
    • 4. Convert Data into spacy format.mp4 (33.9 MB)
    • 4. Convert Data into spacy format.srt (7.4 KB)
    • 5. Testing Entities.mp4 (8.9 MB)
    • 5. Testing Entities.srt (2.0 KB)
    • 6. Convert data into spacy format for all Business card text.mp4 (20.2 MB)
    • 6. Convert data into spacy format for all Business card text.srt (3.2 KB)
    • 7. Splitting Data into Training and Testing Set.mp4 (13.8 MB)
    • 7. Splitting Data into Training and Testing Set.srt (3.6 KB)
    • 8. Spacy Fill the Configuration.mp4 (59.5 MB)
    • 8. Spacy Fill the Configuration.srt (9.2 KB)
    • 9. Spacy Prepare Data.mp4 (48.1 MB)
    • 9. Spacy Prepare Data.srt (10.8 KB)
    5. Predictions
    • 1. Import Required Libraries.mp4 (8.5 MB)
    • 1. Import Required Libraries.srt (2.4 KB)
    • 10. Join token dataframe with Pytesseract data.mp4 (64.1 MB)
    • 10. Join token dataframe with Pytesseract data.srt (12.8 KB)
    • 11. Bounding Box and Tagging Predicted Entities.mp4 (51.4 MB)
    • 11. Bounding Box and Tagging Predicted Entities.srt (8.9 KB)
    • 12. Combine the BIO information.mp4 (44.3 MB)
    • 12. Combine the BIO information.srt (7.9 KB)
    • 13. Bounding Box.mp4 (68.6 MB)
    • 13. Bounding Box.srt (13.2 KB)
    • 2. Clean Text Function.mp4 (5.9 MB)
    • 2. Clean Text Function.srt (1.4 KB)
    • 3. Load Spacy NER Model.mp4 (10.7 MB)
    • 3. Load Spacy NER Model.srt (2.3 KB)
    • 4. Extract Text from Image and Convert into Data Frame.mp4 (36.8 MB)
    • 4. Extract Text from Image and Convert into Data Frame.srt (5.5 KB)
    • 5. Convert Data Frame into Content.mp4 (14.9 MB)
    • 5. Convert Data Frame into Content.srt (2.5 KB)
    • 6. Get Named Entities from model.mp4 (24.1 MB)
    • 6. Get Named Entities from model.srt (3.2 KB)
    • 7. Displacy render.mp4 (7.1 MB)
    • 7. Displacy render.srt (1.0 KB)
    • 8. Tagging Each Word.mp4 (39.3 MB)
    • 8. Tagging Each Word.srt (6.6 KB)
    • 9. Join Label to tokens dataframe.mp4 (24.2 MB)
    • 9. Join Label to tokens dataframe.srt (4.6 KB)
    • Bonus Resources.txt (0.3 KB)

Code:

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