Automating the extraction of key details from invoices using YOLOv4 and Tesseract OCR.
Project Overview
Businesses still manually process digital invoices, which is time-consuming and prone to errors. This project aims to automate the extraction of key details from invoices using advanced machine learning and computer vision techniques. By leveraging YOLOv4 for object detection and Tesseract OCR for text recognition, we can significantly reduce the time and effort required for invoice processing.
Key Achievements
Trained a YOLOv4 model for object detection on a custom dataset of invoices, achieving high accuracy in detecting key fields such as invoice number, date, and total amount.
Implemented Tesseract OCR to accurately recognize and extract text from the detected fields, ensuring reliable data extraction.
Developed an end-to-end automated pipeline that handles invoice upload, object detection, text recognition, and data extraction seamlessly.
Integrated the pipeline with a user-friendly web interface for easy access and operation.
Technology Stack
This project utilizes a variety of technologies and tools to achieve its goals:
Python: The primary programming language used for developing the machine learning models and the automation pipeline.
YOLOv4: A state-of-the-art object detection model used to identify key fields in the invoices.
Tesseract OCR: An optical character recognition engine used to extract text from the detected fields.
OpenCV: A computer vision library used for image processing and manipulation.
PyCharm: An integrated development environment (IDE) used for writing and debugging the code.
Flask: A web framework used to create the web interface for the application.
HTML/CSS: Used for designing the web interface and ensuring a responsive and user-friendly experience.
Project Documentation
Download the comprehensive project report for detailed insights.