muzxxty/document-quality-ocr

📄 Document Quality Assessment System

An intelligent document analysis tool that evaluates the visual quality of scanned or digital documents using Computer Vision and OCR-based techniques.

🚀 Problem & Motivation

💡 Designed for real-world OCR pipelines where input quality directly affects model accuracy.

Poor document quality is one of the biggest reasons OCR systems fail in real-world applications.

This project builds an automated document quality assessment system that analyzes key visual properties—such as contrast, alignment, typography, and spacing—to determine whether a document is suitable for reliable OCR processing.

The system combines multiple independent signals into a unified decision framework, enabling early detection of low-quality inputs before they impact downstream pipelines.

💡 Solution

This project provides an automated system to assess document quality using multi-dimensional analysis:

• —📐 Skew Detection (alignment issues)
• —🔤 Font Consistency Analysis (formatting irregularities)
• —🎨 Text Contrast Evaluation (readability)
• —📏 Line Spacing Analysis (layout structure)
• —📄 Document Type Detection (Printed vs Handwritten)

🧠 Key Features

✔ Multi-metric document evaluation ✔ OCR-based structural analysis ✔ Normalized statistical scoring (robust to layout variations) ✔ Context-aware warnings for handwritten documents ✔ Final decision system: Accept / Review / Reject ✔ Exportable analysis report (CSV) ✔ Interactive UI using Streamlit

🖥️ Demo Workflow

1. 1.Upload a document image (JPG/PNG)
2. 2.System detects document type
3. 3.Performs quality analysis
4. 4.Displays structured report
5. 5.Provides final decision
6. 6.Allows CSV download

📸 Screenshots

📤 Upload Interface

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📊 Analysis Summary

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📌 Final Decision Output

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📊 Sample Output

Final Decision: ✅ ACCEPTABLE

⚙️ Tech Stack

• —Python
• —OpenCV
• —Tesseract OCR
• —NumPy & Pandas
• —Streamlit

▶️ How to Run

git clone https://github.com/mujammilibrahim007-art/Automated-Document-Quality-Assessment-System-for-OCR-Pipelines.git
pip install -r requirements.txt
streamlit run app.py

⚠️ Limitations

• —Designed primarily for printed documents
• —Handwritten text may produce unreliable metrics
• —Heuristic thresholds (can be improved with labeled datasets)

🚀 Future Improvements

• —ML-based document classification
• —Confidence scoring system
• —Layout-aware spacing detection
• —API deployment for real-time processing

🧠 Key Insights

• —Traditional document quality checks rely on raw metrics, which often fail on structured layouts (e.g., headings, bullet points).

• —This system uses normalized statistical measures (coefficient of variation) to distinguish between natural layout variation and actual formatting issues.

• —OCR-based region analysis enables extraction of structural features (font size, spacing, contrast) without relying on language understanding.

• —Document type detection (Printed vs Handwritten) improves reliability by adapting interpretation logic based on OCR confidence.

• —The system demonstrates how multiple weak signals (geometry, typography, clarity) can be combined into a robust decision framework.

⚠️ Note: This system is optimized for printed documents. Handwritten content may produce unreliable metrics.

👨‍💻 Author

Mujammil Ibrahim