Smart Statement Reader Documentation

1. Introduction

The Smart Statement Reader aims to streamline the process of extracting financial data from PDFs, enhancing efficiency and accuracy through AI/ML technologies.

2. Objectives

• Directly process PDFs from ERP/accounting systems.
• Automatically detect and classify PDF formats.
• Extract financial ledger entries into Excel or CSV.
• Reduce manual intervention through high accuracy and self-learning.

3. Problem Solution

3.1. Input and Output

• Input: Raw PDF files containing accounting data.
• Output: Structured formats (Excel/CSV) with accurate financial data.

3.2. Core Features

1. PDF Ingestion:

   • Accepts PDF files directly from accounting systems.

2. AI/ML Models:

   • Structure Detection: Detects and classifies the structure of PDF files (e.g., column layouts, headers, naming conventions).
   • Data Extraction: Extracts financial entries with high accuracy.
   • Layout Handling: Adapts to variations and inconsistencies in document layouts.
   • Self-learning: Improves extraction accuracy based on user feedback over time.
   • File Grouping: Classifies PDFs based on detected formats for streamlined processing.

3. User Feedback Mechanism:

   • Provides a confidence score for extracted data accuracy.
   • Highlights low-confidence entries for user review.
   • Allows users to provide feedback to train the model iteratively.

4. Data Export:

   • Exports processed data into structured formats (Excel/CSV).

4. Implementation

4.1. Technologies Used

• OCR technologies: For PDF data extraction (e.g., Tesseract).
• Machine Learning Frameworks: TensorFlow, PyTorch for format detection and classification.
• User Interface: For uploading files, reviewing results, and providing feedback.

4.2. Functional Components

1. File Uploader: Interface for uploading PDF files.
2. Format Detector: AI model that classifies the structure of PDFs.
3. Data Extractor: OCR combined with ML models to extract data.
4. Feedback System: Mechanism for users to review and provide feedback.
5. Export Module: Generates Excel/CSV files with extracted data.

4.3. Workflow

1. Upload: User uploads a PDF file.
2. Detection: Format detector classifies the structure.
3. Extraction: Data extractor processes the file and extracts financial entries.
4. Review: User reviews low-confidence entries.
5. Feedback: User provides feedback to improve model accuracy.
6. Export: Processed data is exported into Excel/CSV.

5. Evaluation Criteria

• Accuracy: Precision in data extraction and classification from diverse PDF formats.
• Usability: User-friendly interface and easy navigation.
• Scalability: Capability to handle large volumes and varied formats.
• Effectiveness: Improvement in model performance due to the self-learning feedback loop.

6. Challenges and Constraints

• Diversity in PDF Formats: Managing various document layouts and data.
• Data Noise: Ensuring robustness against noisy or incomplete data.
• Processing Speed: Balancing high accuracy with real-time output within a few seconds.

7. Expected Outcome

• High accuracy in detecting, classifying, and extracting data from PDFs.
• A self-improving model through user feedback.
• Seamless export of finished data into structured formats.