Best practice for batch OCR processing with memory optimization?

[DefcoGit]

Hi,

I am working on processing large batches of scanned documents (500+ pages) with german-ocr and running into memory issues.

Current setup:

• Python 3.11
• 16GB RAM
• Processing PDFs with ~50 pages each

Questions:

1. What is the recommended batch size for optimal memory usage?
2. Should I use multiprocessing or async for parallel processing?
3. Are there any caching strategies for repeated document types (invoices, forms)?

Any best practices for production deployments would be greatly appreciated!

Thanks!

[Keyvanhardani]

Great questions! Here are my recommendations based on production experience:

1. Batch Size Optimization

For 16GB RAM, I recommend:

• 10-15 pages per batch for high-resolution scans (300+ DPI)
• 20-30 pages per batch for standard resolution (150 DPI)

from german_ocr import OCREngine
import gc

def process_in_batches(pages, batch_size=15):
    results = []
    for i in range(0, len(pages), batch_size):
        batch = pages[i:i+batch_size]
        results.extend(engine.process(batch))
        gc.collect()  # Force garbage collection
    return results

2. Multiprocessing vs Async

Use multiprocessing for CPU-bound OCR tasks:

from concurrent.futures import ProcessPoolExecutor
import os

workers = max(1, os.cpu_count() - 1)  # Leave one core free

with ProcessPoolExecutor(max_workers=workers) as executor:
    results = list(executor.map(process_page, pages))

Async is better for I/O-bound tasks (file loading, API calls).

3. Caching Strategy

For repeated document types, use template caching:

import hashlib
from functools import lru_cache

@lru_cache(maxsize=100)
def get_layout_template(doc_hash):
    # Cache detected layouts for similar documents
    return detect_layout(doc_hash)

def get_doc_hash(image):
    # Hash based on structure, not pixel values
    return hashlib.md5(image.tobytes()[:1000]).hexdigest()

Production Tips

• Use memory-mapped files for large PDFs
• Enable streaming mode if available
• Monitor with psutil to prevent OOM
• Consider Redis for distributed caching

Hope this helps! Let me know if you need more details.