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OCRmyPDF Tutorial: Convert Scanned Documents into Searchable PDF/A Files with Sidecar Text Extraction and Batch Processing
In this tutorial, we build an advanced, self-contained OCRmyPDF workflow to convert scanned documents into searchable PDF/A files, extract sidecar text, and process multiple documents in batch. As of 2026, OCRmyPDF remains a leading open-source tool for OCR tasks, supporting both English and multilingual text recognition via Tesseract. This guide covers installation, configuration, and automation for efficient document digitization. We use a practical Jupyter notebook example (linked below) to demonstrate the pipeline, including sidecar text extraction for downstream text analysis and batch processing for handling large document sets. By the end, you will be able to: (1) convert scanned PDFs or images into searchable PDF/A-1b compliant files, (2) extract text sidecars as separate .txt files, and (3) process multiple documents in a single command or script. All code is tested with Python 3.10+ and the latest OCRmyPDF 16.x release (April 2026).
## Table of Contents
- Setup and Installation
- Basic Single-File Conversion
- Sidecar Text Extraction
- Batch Processing
- Advanced Options (Language, Image Optimization)
- Use Cases and Automation Tips
## Setup and Installation
Ensure you have Python 3.10 or newer and install OCRmyPDF via pip:
`pip install ocrmypdf`
Additionally, install Tesseract OCR and its language data (e.g., English, Arabic) via your system package manager or conda.
## Basic Single-File Conversion
Convert a scanned PDF to a searchable PDF/A file:
`ocrmypdf --output-type pdfa input.pdf output.pdf`
For images (e.g., JPEG), OCRmyPDF automatically converts to PDF before processing.
## Sidecar Text Extraction
Generate a separate text file alongside the PDF:
`ocrmypdf --sidecar output.txt input.pdf output.pdf`
This extracts OCR text into a .txt file while still creating the searchable PDF.
## Batch Processing
Use a shell script or Python loop to process multiple files:
```bash
for f in *.pdf; do ocrmypdf "$f" "${f%.pdf}_ocr.pdf"; done
```
Or with Python:
```python
import subprocess, glob
for file in glob.glob('*.pdf'):
subprocess.run(['ocrmypdf', file, file.replace('.pdf', '_ocr.pdf')])
```
## Advanced Options
- Specify language: `--language eng+ara` for bilingual documents.
- Optimize for web: `--optimize 1` (JPEG quality).
- Keep intermediate files: `--keep-temporary-files` for debugging.
## Use Cases and Automation Tips
Ideal for digitizing historical archives, legal documents, or invoices. In 2026, OCRmyPDF integrates well with CI/CD pipelines (e.g., GitHub Actions) for automated processing. Always verify output via `pdfinfo` or `pdftotext` to ensure searchability.
For the complete interactive notebook and additional examples, visit the linked GitHub repository.