By taking this course, you will learn how to do Smart Data Extraction from PDFs and Images.
The world’s technology has put cognitive skills at the top of the list, with a lot of attention paid to intelligent data extraction. This gets more complicated because there are so many different types of documents that can be used, like pdf documents with structured data, scanned pdf documents, and Word documents. This class aims to help you understand these different formats and then teach you how to do smart data extraction with Python, Pandas, OCR, Tesseract, PyTesseract, OpenCV, Spacy, and NER concepts.
The course will show you how to build a common pipeline even though your data comes in different formats. You’ll learn how to extract data using OCR, label data with Spacy, and train a model with custom NER data. Then, you’ll use the model to predict what your data will look like. Then, in the end, we’ll put all the things we learned together to make a Smart Text Extractor app.
In this course, you will learn about the text data extraction process in great detail. First, you will learn about the technology concepts, and then you will write code to show how these concepts are used. A detailed code walkthrough has been included for all of the code implementations, and 12 source code files that go along with them can be found on the site. In addition, the quiz at the end of the course lets you see how well you did and where you need to improve.
Who this course is for:
- Python coders who want to learn how to extract data from text using OCR.
- NLP and NER enthusiasts who want to learn more about text labelling in computer vision.
- The OCR Engineer