gemini-ocr 0.1.1

Hybrid OCR with gemini and DocumentAI

Gemini OCR

Traceable Generative Markdown for PDFs

Gemini OCR is a library designed to convert PDF documents into clean, semantic Markdown while maintaining precise traceability back to the source coordinates. It bridges the gap between the readability of Generative AI (Gemini, Document AI Chunking) and the grounded accuracy of traditional OCR (Google Document AI).

Key Features

• Generative Markdown: Uses Google's Gemini Pro or Document AI Layout models to generate human-readable Markdown with proper structure (headers, tables, lists).
• Precision Traceability: Aligns the generated Markdown text back to the original PDF coordinates using detailed OCR data from Google Document AI.
• Reverse-Alignment Algorithm: Implements a robust "reverse-alignment" strategy that starts with the readable text and finds the corresponding bounding boxes, ensuring the Markdown is the ground truth for content.
• Confidence Metrics: (New) Includes coverage metrics to quantify how much of the Markdown content is successfully backed by OCR data.
• Pagination Support: Automatically handles PDF page splitting and merging logic.

Architecture

The library processes documents in two parallel streams:

1. Semantic Stream: The PDF is sent to a Generative AI model (e.g., Gemini 2.5 Flash) to produce a clean Markdown representation.
2. Positional Stream: The PDF is sent to Google Document AI to extract raw bounding boxes and text segments.

These two streams are then merged using a custom alignment engine (seq_smith + bbox_alignment.py) which:

1. Normalizes both text sources.
2. Identifies "anchor" comparisons for reliable alignment.
3. Computes a global alignment using the anchors to constrain the search space.
4. Identifies significant gaps or mismatches.
5. Recursively re-aligns mismatched regions until a high-quality alignment is achieved.

Key Features:

• Robust to Cleanliness Issues: Handles extra headers/footers, watermarks, and noisy OCR artifacts.
• Scale-Invariant: Recursion ensures even small missed sections in large documents are recovered.

Quick Start

import asyncio
from pathlib import Path
from gemini_ocr import gemini_ocr, settings

async def main():
    # Configure settings
    ocr_settings = settings.Settings(
        project="my-gcp-project",
        location="us",
        gcp_project_id="my-gcp-project",
        layout_processor_id="projects/.../processors/...",
        ocr_processor_id="projects/.../processors/...",
        mode=settings.OcrMode.GEMINI,
    )

    file_path = Path("path/to/document.pdf")

    # Process the document
    result = await gemini_ocr.process_document(ocr_settings, file_path)

    # Access results
    print(f"Coverage: {result.coverage_percent:.2%}")

    # Get annotated HTML-compatible Markdown
    annotated_md = result.annotate()
    print(annotated_md[:500])  # View first 500 chars

if __name__ == "__main__":
    asyncio.run(main())

Configuration

The gemini_ocr.settings.Settings class controls the behavior:

Parameter	Type	Description
project	str	GCP Project Name
location	str	GCP Location (e.g., us, eu)
gcp_project_id	str	GCP Project ID (might be same as project)
layout_processor_id	str	Document AI Processor ID for Layout (if using DOCUMENTAI mode)
ocr_processor_id	str	Document AI Processor ID for OCR (required for bounding boxes)
mode	OcrMode	GEMINI (default), DOCUMENTAI, or DOCLING
gemini_model_name	str	Gemini model to use (default: gemini-2.5-flash)
alignment_uniqueness_threshold	float	Min score ratio for unique match (default: 0.5)
alignment_min_overlap	float	Min overlap fraction for valid match (default: 0.9)
include_bboxes	bool	Whether to perform alignment (default: True)
markdown_page_batch_size	int	Pages per batch for Markdown generation (default: 10)
ocr_page_batch_size	int	Pages per batch for OCR (default: 10)
num_jobs	int	Max concurrent jobs (default: 10)
cache_dir	str	Directory to store API response cache (default: .docai_cache)

License

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