The problem Billify solves

Billify is an advanced solution designed to convert raw OCR text from receipts and invoices into structured JSON format. By leveraging the power of OCR technology and a fine-tuned large language model (LLM), Billify automates data extraction and processing from physical documents, offering high efficiency and accuracy. This project utilizes PaddleOCR for text extraction and a fine-tuned LLM from Hugging Face Transformers for converting raw OCR text into structured JSON format.

• Image Input: Supports common image formats (JPEG, PNG, PDF) and batch processing.
• OCR Text Extraction: Uses PaddleOCR to handle various layouts and fonts in receipts and invoices.
• Structured JSON Output: Extracts key information such as store name, date, items, quantities, prices, and total amount.
• Error Handling: Robust error management for unclear or erroneous OCR results.
• Scalability: Designed to handle large volumes of images efficiently.
• Security: Ensures data privacy and compliance with data protection regulations.

The system is organized into the following modules:

• Image Input Module: Handles the upload and storage of image files.
• OCR Processing Module: Uses PaddleOCR to extract text from images.
• Text-to-JSON Conversion Module: Employs a fine-tuned LLM to convert raw OCR text into structured JSON format.
• Output Module: Provides the structured JSON data to the user or integrates it with existing business systems.

Demand increasing for automation in data entry and processing within various industries such as retail, finance, and logistics provides significant market opportunity due to businesses seeking ways to reduce operational costs and improve efficiency. Billify is a powerful tool designed to automate and streamline the extraction of structured data, enhancing operational efficiency and accuracy.

Attributions:

• http://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
• http://github.com/PaddlePaddle/PaddleOCR
• https://github.com/libo-coder/PaddleOCR

Challenges we ran into

Technical Risks: Potential challenges in handling diverse receipt formats and maintaining accuracy.
Mitigation Strategies: Continuous model training and updates, extensive testing on diverse datasets.