Great question—Arabic OCR has unique challenges (ligatures, diacritics, right-to-left script) that get amplified with low-quality fonts or complex layouts. Let’s break down actionable solutions for each part of your workflow:

1. Preprocessing: Fix the Source Before OCR #

Preprocessing is often the most impactful step for low-quality inputs. Here’s what to focus on:

Image/PDF Enhancement #

• Increase Resolution: For scanned PDFs or low-res digital outputs, convert pages to high-DPI images (300+ DPI) using Ghostscript:

  gs -dSAFER -dBATCH -dNOPAUSE -r300 -sDEVICE=png16m -sOutputFile=page-%03d.png input.pdf
  

• Contrast & Noise Reduction: Use ImageMagick to sharpen text and reduce background noise:

  convert page-001.png -contrast-stretch 2% -deskew 40% -threshold 50% cleaned-page-001.png
  

  The -deskew flag fixes tilted text, a common source of OCR errors in Arabic script.
• Layout Segmentation: For PDFs with mixed text/images or complex columns, use PyMuPDF (fitz) to split pages into text-only regions before running OCR. This avoids the engine misinterpreting non-text elements as characters.

Font Normalization (For Digital PDFs) #

If your PDF is digital (not scanned) but uses broken or non-standard fonts:

• First, try extracting raw text with Poppler’s pdftotext using the -layout flag to preserve structure:

  pdftotext -layout input.pdf raw-text.txt
  

  Use this raw text as a baseline, then run OCR only on sections where pdftotext failed (e.g., garbled characters).
• If fonts are completely unreadable, convert the PDF to images (as above) and apply the image enhancement steps.

2. OCR Engine Tuning #

Surya is solid, but tweaking it or combining it with other engines can boost accuracy:

• Use Arabic-Specific Models: Ensure Surya is using its pre-trained Arabic model (check documentation for model loading flags). For comparison, Tesseract has a well-trained Arabic model—you can run both engines and merge results for critical documents.
• Adjust Layout Parameters: For Tesseract, use --psm (page segmentation mode) to match your layout:

  tesseract cleaned-page-001.png output -l ara --psm 6
  

  --psm 6 assumes a single uniform text block, which works well for most Arabic documents. For multi-column layouts, use --psm 3 (default) or --psm 4 (assume a single column of text).
• Fine-Tune on Custom Data: If you have a dataset of corrected Arabic OCR pairs, fine-tune Surya or Tesseract to your specific font styles. This is especially useful for niche fonts or specialized document types.

3. LLM-Based Post-Processing: Do It Right #

Your current LLM approach didn’t work because generic prompts don’t account for Arabic’s unique script nuances. Here’s how to fix it:

• Tailor Prompts for Arabic OCR Correction: Use a prompt that explicitly guides the model to fix common Arabic OCR errors (ligature mix-ups, missing diacritics, reversed characters):

  You are an expert in Arabic language and OCR error correction. Below is text extracted from OCR with possible mistakes (wrong ligatures, missing letters, incorrect tashkeel, reversed characters). Correct it to standard, grammatically accurate Arabic while preserving the original meaning. Output only the corrected text—no explanations.
  OCR Text: [paste your extracted text here]

• Use Arabic-Optimized Models: Stick to models trained extensively on Arabic text, like Aya (try the 7B or 13B parameter versions) or Arabic BERT fine-tuned for text correction. For Ollama, pull the latest Aya model and use the prompt above.
• Fine-Tune a Small Model: If you have a dataset of corrected OCR pairs, fine-tune a lightweight model (e.g., Mistral-7B) using LoRA for faster, more targeted correction. Tools like transformers and peft make this manageable.

4. Final Best Practices #

• Build a Pipeline: Combine preprocessing → tuned OCR → LLM correction into an automated workflow (e.g., using Python scripts with PyMuPDF, Surya, and Ollama’s API).
• Validate with Test Data: Create a test set of low-quality Arabic PDFs with known correct text. Measure accuracy at each step to identify bottlenecks.
• Prioritize Diacritics: If your documents include tashkeel, ensure your OCR model and LLM are configured to handle them—many default models ignore diacritics unless explicitly enabled.

内容的提问来源于stack exchange，提问作者Marwa