acatrack-pdf-parser-rs 0.1.0

High-performance, native parallel Rust PDF parser engine for VTU provisional results

⚡ acatrack-pdf-parser-rs

A high-performance, native Rust PDF parsing engine designed specifically to extract structured academic student records and provisional exam marks. Bridged seamlessly to Python via PyO3 and Maturin, it leverages multi-threaded CPU parallel processing via Rayon to slash batch ingestion processing times.

Developed as the core ingestion engine of AcaTrack, this parser solves complex visual layout alignment issues mathematically and runs 38.4x faster than traditional sequential Python parsers.

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🚀 Key Features

• 🏎️ Rayon Parallelization: GIL-free multi-threaded PDF table extraction using CPU core saturation.
• 🛡️ Spacing-Robust Digit Concatenation: Reconstructs fragmented, narrow visual columns (e.g., visual layout splits like "4" and "5" for a score of 45) automatically.
• 📐 Virtual Row Splitting: Automatically splits stacked cell values separated by newlines (\n) into neat, index-aligned rows.
• 📐 Mathematical Verification: Automatically executes algebraic checksum checks ($\text{IA} + \text{SEE} == \text{Total}$) to guarantee $100%$ parsing accuracy.
• ⚡ PyO3 FFI Bridge: Compiled into a native .so / .pyd module that can be imported directly in Python with zero performance loss.
• 📊 FFI Telemetry: Streams granular Rust execution logs back into Python for instant diagnostic debugging.

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📊 Performance Benchmarks (1,308 PDFs)

Tested over 1,308 PDFs (across 4 ZIP upload requests) containing freshman provisional university results:

Metric	Sequential Python Core	Parallel Rust Engine (This Library) 🚀	Net Improvement
Total Parsing Duration	21.78 minutes	34.06 seconds (~0.57 min)	38.4x Faster 🚀
Speed per PDF	0.9992 seconds	0.0260 seconds	38.4x Faster 🚀
Memory Net Impact	+268.48 MB	+76.43 MB	71.5% Lower RAM 📉

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🛠️ Architecture

The parser integrates a dual-tier parsing fallback mechanism to remain robust across layout changes:

graph TD
    A[Raw PDF Page] --> B{Tier 1: Clean Column Scan}
    B -- Found Code & Split Cells --> C[Unified Token Concatenation & Math Verification]
    B -- Layout Grid Failure --> D{Tier 2: Fallback Flat Text Scan}
    D --> E[Tokenize flat whitespace stream]
    E --> F[Match codes & parse trailing numeric pairs]
    C --> G[StudentRecord PyDict Object]
    F --> G

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📦 Getting Started

Prerequisites

• Rust Toolchain: rustup, rustc, cargo (Latest stable edition)
• Python: 3.8+
• Maturin: pip install maturin

Local Development & Setup

1. Clone the repository:

   git clone https://github.com/chetanuchiha16/acatrack-pdf-parser-rs.git
   cd acatrack-pdf-parser-rs
   

2. Compile and install locally into your active Python environment:

   # Builds in release mode and sets up an editable package link
   maturin develop --release
   

3. Verify compilation:

   python -c "import acatrack_rust; print(acatrack_rust.__doc__)"
   

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🐍 Python Usage Example

import acatrack_rust

# Target subjects to scan for
target_subjects = ["BMATS101", "BCHES102", "BCEDK103", "BENGK106"]

# Parse a single PDF file
record = acatrack_rust.parse_single_pdf(
    pdf_path="path/to/student_result.pdf",
    subject_codes=target_subjects
)

if record:
    print(f"USN: {record['usn']}")
    print(f"Name: {record['name']}")
    print(f"Marks Extracted: {record['marks']}")
    print("\n--- Telemetry Logs ---")
    for log in record['logs']:
        print(log)

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📄 License

Licensed under the MIT License.