Billify (Receipt Radar)

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.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Architecture
• Technical Details
• Business Feasibility
• License

Introduction

Billify automates the process of extracting structured data from receipts and invoices. 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.

Features

• 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.

Installation

To set up Billify, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/Billify.git
   cd Billify

2. Install required libraries:

   • Install Hugging Face Transformers:

     pip install git+https://github.com/huggingface/transformers.git
     pip install accelerate

   • Install PaddleOCR:

     git clone https://github.com/PaddlePaddle/PaddleOCR.git
     cd PaddleOCR
     python3 -m pip install paddlepaddle-gpu
     pip install "paddleocr>=2.0.1"

Usage

1. Import necessary libraries:

   import torch
   from paddleocr import PaddleOCR
   from transformers import pipeline

2. Initialize PaddleOCR:

   ocr = PaddleOCR(use_angle_cls=True, lang='en')

3. Extract OCR text from an image:

   img_path = 'path_to_receipt_image.jpg'
   ocr_result = ocr.ocr(img_path, cls=True)
   raw_text = " ".join([line[1][0] for line in ocr_result[0]])

4. Convert raw text to structured JSON using the fine-tuned LLM:

   model = pipeline('text2json', model='zephyr-7b-alpha')
   def convert_to_json(raw_text):
       structured_data = model(raw_text)
       return structured_data
   structured_json = convert_to_json(raw_text)

Architecture

The system is organized into the following modules:

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

Technical Details

Languages and Libraries

• Languages: Python
• Libraries:
  • OCR: PaddleOCR
  • LLM: Hugging Face Transformers
• Hardware: GPUs for efficient processing

Performance and Scalability

• Performance: The system is optimized for minimal latency and high throughput.
• Scalability: Designed to handle large volumes of images efficiently.

Security

• Ensures data privacy and security during processing and storage.
• Compliance with relevant data protection regulations is maintained.

Business Feasibility

Market Analysis

• Demand: Increasing need for automation in data entry and processing within various industries such as retail, finance, and logistics.
• Growth Potential: Significant market opportunity due to businesses seeking ways to reduce operational costs and improve efficiency.

Competitive Advantage

• Accuracy: High accuracy in data extraction by combining state-of-the-art OCR and LLM technology.
• Efficiency: Quick processing of large volumes of receipts and invoices.
• Flexibility: Adaptable to various receipt and invoice formats.

Cost-Benefit Analysis

• Implementation Costs: Investment in development, hardware (GPUs), and ongoing maintenance.
• Operational Savings: Significant reduction in manual data entry costs and time.
• ROI: High return on investment due to improved efficiency and accuracy.

Risk Analysis

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

License

This project is licensed under the MIT License. See the LICENSE file for details.

---

Billify is a powerful tool designed to automate and streamline the extraction of structured data from receipts and invoices, enhancing operational efficiency and accuracy.