Bill Wizard (Bill Amount Detection - OCR, Python, Tesseract)
Create a desktop application that captures video input (from a webcam or pre-recorded video) to detect the total bill amount from invoices and categorize the invoices based on their content. The system should use Optical Character Recognition (OCR) to extract text and employ machine learning to categorize the invoice.
Requirements
Video Input: Use a webcam or pre-recorded video to capture the invoice, providing real-time or near-real-time analysis.
OCR for Bill Amount Detection: Use Tesseract OCR to detect and extract the total amount from the invoice.
Invoice Categorization: Use machine learning algorithms (e.g., logistic regression, SVM) to classify the invoice into predefined categories (e.g., groceries, electronics, services) based on text content.
Data Export: Allow users to export detected amounts and categories in structured formats (JSON, CSV, XML) for further use.
Tech Stack
Programming Language: Python
Libraries: OpenCV for video input, Tesseract for OCR to extract text, and Scikit-learn or TensorFlow for invoice categorization based on extracted data.
Work Flow
Step 1: Capture video frames using OpenCV. Analyze the frames to extract the relevant portions of the invoice (total amount, item list, etc.).
Step 2: Apply Tesseract OCR to extract the total bill amount and other relevant text from the invoice.
Step 3: Use Scikit-learn or TensorFlow to train a machine learning model for categorizing invoices based on extracted text data.
Step 4: Export the extracted data (amounts, categories) in CSV, JSON, or XML for downstream processing.
Evaluation Criteria
OCR Accuracy: How effectively does the system recognize the total bill amount from various invoice formats?
Categorization Efficiency: How well does the system categorize the invoices based on the content?
Export Functionality: Does the tool provide flexible and easy-to-use export options for the data?
User Experience: Is the tool intuitive and fast enough for practical use in real-time scenarios?