https://github.com/precise-goals/smartpdfreader

The objective is to build an AI/ML-powered Smart Statement Reader solution that directly processes PDFs extracted from ERP/accounting systems, automatically detecting and classifying file formats, and accurately extracting tabular financial entries into structured formats like Excel or CSV.
https://github.com/precise-goals/smartpdfreader

Last synced: about 1 year ago
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The objective is to build an AI/ML-powered Smart Statement Reader solution that directly processes PDFs extracted from ERP/accounting systems, automatically detecting and classifying file formats, and accurately extracting tabular financial entries into structured formats like Excel or CSV.

• Host: GitHub
• URL: https://github.com/precise-goals/smartpdfreader
• Owner: Precise-Goals
• License: mit
• Created: 2025-01-24T16:40:59.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-24T16:52:54.000Z (about 1 year ago)
• Last Synced: 2025-01-24T17:31:46.517Z (about 1 year ago)
• Language: Python
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 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.