What you’ll learn:
Understand data extraction from different types of documents such as PDF, Word and Scanned Images
Learn how to use Tesseract and PyTesseract for recognition of data from images
Learn how to use Spacy efficiently for labelling along with training on custom data for NER
Use Pandas to convert extracted data to a CSV format
Requirements:
Basic Python Programming knowledge
Description:
Gain a competitive edge in the world of Computer Vision through this course by learning how to do Smart Data Extraction from Pdf and Images.
The technology landscape of world has brought in cognitive skills at the forefront where major emphasis is on intelligent data extraction. This becomes more complex due to the huge variety of input documents such as pdf document with structured data, scanned pdf document and word document. This course aims to solve this challenging problem by helping you to understand these various formats and then empower you to do smart data extraction using Python, Pandas, OCR, Tesseract, PyTesseract, OpenCV, Spacy and NER concepts.
The course will guide you on how you can build a common pipeline irrespective of multiple data formats through a structured workflow wherein you will learn Data Extraction using OCR, Data Labelling with Spacy along with Training a model on custom NER data and validating the model through prediction. Towards the end, we will combine all the learnings to build a Smart Text Extractor application.
The course has been designed to explain text data extraction workflow in depth by first explaining the technology concepts and then their implementation through code. Detailed code walkthrough has been included for all the code implementations and 12 supporting source code files are available for download. In addition to this, the quiz at the end of course helps you to assess your knowledge and identify the improvement areas.
Enroll in this course and enhance your cognitive capabilities. Here are just few of the topics we will be learning:
