Add ADR 0047 for OCR engine selection and fix requested changes from JabRef/user-documentation#577

Author: Kaan0029

docs/decisions/0047-ocr-engine-selection.md

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+---
+parent: Decision Records
+nav_order: 47
+---
+# OCR Engine Selection for JabRef
+
+## Context and Problem Statement
+
+JabRef requires an OCR engine to extract text from scanned PDFs and image-based academic documents. Tesseract is currently implemented, but accuracy varies significantly with document quality and type. Academic documents present unique challenges: mathematical notation, multiple languages, complex layouts, tables, and mixed handwritten/printed content. Which OCR engine(s) should JabRef adopt to best serve its academic user base while balancing accuracy, cost, privacy, and implementation complexity?
+
+## Decision Drivers
+
+* Accuracy on academic document types (printed papers, scanned books, handwritten notes)
+* Privacy requirements for unpublished research materials
+* Cost constraints (open-source project with limited funding)
+* Language support for international academic community
+* Support for mathematical and scientific notation
+* Offline capability for secure research environments
+* Processing speed for batch operations
+* Implementation and maintenance complexity
+* Table and structure extraction capabilities
+* Long-term sustainability and community support
+
+## Considered Options
+
+* Option 1: Tesseract OCR (current implementation)
+* Option 2: Google Cloud Vision API
+* Option 3: AWS Textract
+* Option 4: Microsoft Azure Computer Vision
+* Option 5: EasyOCR
+* Option 6: PaddleOCR
+* Option 7: ABBYY FineReader SDK
+
+### Pros and Cons of the Options
+
+#### Option 1: Tesseract OCR
+
+Originally developed by Hewlett‑Packard as proprietary software in the 1980s, released as open source in 2005 and development was sponsored by Google in 2006.
+
+* **Good**, because Version 4 adds LSTM‑based OCR engine and models for many additional languages and scripts, bringing the total to 116 languages. Additionally 37 scripts are supported
+* **Good**, because completely free and open‑source (Apache 2.0 license)
+* **Good**, because runs entirely offline preserving document privacy
+* **Good**, because has an active developer community that regularly updates it, fixes bugs, and improves performance based on user feedback
+* **Good**, because supports over 100 languages out of the box and can be trained to recognize additional languages or custom fonts
+* **Good**, because Tesseract can process right‑to‑left text such as Arabic or Hebrew, many Indic scripts as well as CJK quite well
+* **Neutral**, because Tesseract OCR is an open‑source product that can be used for free
+* **Neutral**, because preprocessing can boost the character‑level accuracy of Tesseract 4.0 from 0.134 to 0.616 (+359 % relative change) and the F1 score from 0.163 to 0.729 (+347 % relative change)
+* **Bad**, because Tesseract OCR recognizes the text in the well‑scanned email pretty well. However, for handwritten letters or smartphone‑captured documents it may output nonsense or nothing
+* **Bad**, because performs best on documents with straightforward layouts but may struggle with complex layouts, requiring additional pre‑ or post‑processing
+* **Bad**, because it may perform poorer on noisy scans compared to other solutions
+
+##### Sources
+
+* https://www.mdpi.com/2073-8994/12/5/715
+* https://nanonets.com/blog/ocr-with-tesseract/
+* https://en.wikipedia.org/wiki/Tesseract_(software)
+
+#### Option 2: Google Cloud Vision API
+
+Cloud‑based OCR service from Google.
+
+* **Good**, because Vision OCR reaches 98 % text accuracy on a diverse data set
+* **Good**, because it performs well across complex, multilingual, and handwritten documents without language hints
+* **Good**, because language support rivals Azure and Tesseract and surpasses AWS Textract
+* **Good**, because it is the only viable option among the tested engines for reliable handwriting recognition
+* **Neutral**, because costs \$1.50 per 1 000 pages (first 1 000 pages free) and new customers get \$300 credit (~200 000 pages)
+* **Bad**, because requires an internet connection and a Google Cloud account
+* **Bad**, because documents are processed on Google servers (privacy concern)
+
+##### Sources
+
+* https://nanonets.com/blog/ocr-with-tesseract/
+* https://www.plugger.ai/blog/comparison-of-paddle-ocr-easyocr-kerasocr-and-tesseract-ocr
+* https://en.wikipedia.org/wiki/Tesseract_(software)
+
+#### Option 3: AWS Textract
+
+Amazon’s document‑analysis service.
+
+* **Good**, because moderately outperforms others on noisy scans
+* **Good**, because excels at extracting tables and form fields
+* **Good**, because focuses on scanned, structured documents (e.g., forms)
+* **Neutral**, because pricing similar to Google
+* **Bad**, because limited support for non‑Latin scripts
+* **Bad**, because handwriting recognition is weak
+
+##### Sources
+
+* https://news.ycombinator.com/item?id=20470439
+* https://blog.roboflow.com/best-ocr-models-text-recognition/
+* https://unstract.com/blog/guide-to-optical-character-recognition-with-tesseract-ocr/
+* https://nanonets.com/blog/ocr-with-tesseract/
+* https://www.plugger.ai/blog/comparison-of-paddle-ocr-easyocr-kerasocr-and-tesseract-ocr
+* https://en.wikipedia.org/wiki/Tesseract_(software)
+
+#### Option 4: Microsoft Azure Computer Vision
+
+* **Good**, because leads Category 1 (digital screenshots) with 99.8 % accuracy
+* **Good**, because handles complex layouts (invoices, receipts, ID cards) well
+* **Good**, because supports 25+ languages
+* **Bad**, because handwriting recognition is poor
+* **Bad**, because requires cloud processing
+
+##### Sources
+
+* https://nanonets.com/blog/ocr-with-tesseract/
+* https://blog.roboflow.com/best-ocr-models-text-recognition/
+* https://dida.do/blog/comparison-of-ocr-tools-how-to-choose-the-best-tool-for-your-project
+
+#### Option 5: EasyOCR
+
+* **Good**, because among open‑source engines it often matches or exceeds peers
+* **Good**, because supports 70+ languages and runs locally
+* **Good**, because optimized for speed, enabling real‑time processing
+* **Bad**, because still trails top LMMs in pure accuracy
+* **Bad**, because limited published benchmark data
+
+##### Sources
+
+* https://www.klippa.com/en/blog/information/tesseract-ocr/
+
+#### Option 6: PaddleOCR
+
+* **Good**, because achieves state‑of‑the‑art scores on ICDAR benchmarks
+* **Good**, because supports major Asian and Latin scripts and is fast
+* **Bad**, because supports fewer languages than Tesseract or EasyOCR
+* **Bad**, because community is smaller and ecosystem less mature
+
+##### Sources
+
+* https://dida.do/blog/comparison-of-ocr-tools-how-to-choose-the-best-tool-for-your-project
+
+#### Option 7: ABBYY FineReader SDK
+
+* **Good**, because preserves document structure (tables, zones) in output
+* **Good**, because excels at tabular data extraction
+* **Neutral**, because commercial licensing required
+* **Bad**, because handwriting recognition is very poor
+* **Bad**, because high cost (pricing not publicly listed)
+
+##### Sources
+
+* https://nanonets.com/blog/ocr-with-tesseract/
+* https://en.wikipedia.org/wiki/Tesseract_(software)
+
+## Decision Outcome
+
+Chosen option: "Option 1: Tesseract OCR", with planned addition of "Option 2: Google Cloud Vision API" as an optional premium feature, because Tesseract provides a solid free foundation while Google Vision offers superior accuracy for users willing to trade privacy for performance.
+
+### Consequences
+
+* **Good**, because maintains free, privacy-preserving option as default
+* **Good**, because allows users to opt-in to higher accuracy when needed
+* **Good**, because Tesseract's 100+ language support covers academic needs
+* **Good**, because implementation is already complete and tested
+* **Bad**, because Tesseract struggles with handwritten text
+* **Bad**, because requires additional development for cloud integration
+* **Bad**, because increases support complexity with multiple engines
+
+## Full Source Overview
+
+The web resources that informed this ADR:
+
+1. <https://www.mdpi.com/2073-8994/12/5/715>
+2. <https://nanonets.com/blog/ocr-with-tesseract/>
+3. <https://en.wikipedia.org/wiki/Tesseract_(software)>
+4. <https://www.plugger.ai/blog/comparison-of-paddle-ocr-easyocr-kerasocr-and-tesseract-ocr>
+5. <https://news.ycombinator.com/item?id=20470439>
+6. <https://blog.roboflow.com/best-ocr-models-text-recognition/>
+7. <https://unstract.com/blog/guide-to-optical-character-recognition-with-tesseract-ocr/>
+8. <https://dida.do/blog/comparison-of-ocr-tools-how-to-choose-the-best-tool-for-your-project>
+9. <https://www.klippa.com/en/blog/information/tesseract-ocr/>