Enterprise AI Analysis
Old Greek OCR Result Correction Using LLMs
This analysis explores the efficacy of Large Language Models (LLMs) in post-correcting Optical Character Recognition (OCR) errors in historical Old Greek documents. We investigate performance across varied datasets and OCR accuracy levels, highlighting significant potential for enhancing the quality of digitized historical texts.
Executive Impact: Enhanced Document Accuracy
LLMs offer a powerful solution to a long-standing challenge in digitizing historical archives. By significantly reducing Character Error Rates, this technology unlocks greater research potential and preserves cultural heritage with unprecedented precision.
0
Avg. CER Improvement
0
Avg. WER Improvement
0
Languages Supported (Indirectly)
0
Documents Processed (Simulated)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Experimental Outcomes
Dataset Overview
LLM-Powered OCR Correction Process
Our approach leverages state-of-the-art Deep Neural Networks for initial OCR, followed by advanced Large Language Models for a sophisticated post-correction phase. This multi-step process is designed to overcome the unique challenges of historical Old Greek documents.
Enterprise Process Flow
Text Line Segmentation (YOLOv5-OBB)
→
Deep Neural Network OCR (Calamari-HTR+)
→
OCR Output Preprocessing
→
LLM Correction Pipeline (Prompt Engineering & In-context Learning)
→
Enhanced OCR Output
The core methodology integrates robust image processing for text detection with advanced sequence-to-sequence OCR. The crucial innovation lies in feeding this initial output to LLMs, which, through carefully crafted prompts and in-context examples, refine the text by applying linguistic knowledge far beyond what traditional OCR can achieve.
Key Experimental Findings
Our experiments reveal significant improvements in OCR accuracy for historical Old Greek documents, especially when baseline OCR performance is low. LLMs demonstrate a powerful ability to correct errors and contextualize text, leading to cleaner, more reliable digitized content.
1.36%
Absolute CER Reduction for ShakeIT Dataset (Gemini-2.0-flash)
OCR Accuracy Improvement Across Datasets
| Dataset | Metric | OCR Base (%) | LLM Corrected (Best) (%) | Improvement (%) |
|---|---|---|---|---|
| ShakeIT | CER | 4.21 | 2.85 | 32.3 |
| ShakeIT | WER | 20.14 | 12.87 | 36.1 |
| HParliament | CER | 3.68 | 2.61 | 29.1 |
| HParliament | WER | 18.10 | 9.21 | 49.1 |
Note: Improvement calculated as ((OCR Base - LLM Corrected) / OCR Base) * 100.
Strategic Application of LLMs: Maximizing Impact on Noisy Data
Our findings indicate that LLMs provide the most significant quality enhancement when the initial OCR accuracy is relatively low (Character Error Rate > 2.5%). This suggests a targeted application strategy, where LLM post-correction is prioritized for documents with higher initial error rates, optimizing computational resources and maximizing impact on the most challenging historical texts. For already high-quality OCR, LLM impact is minimal or can even degrade performance due to minor spelling variations, indicating the need for smart orchestration.
Old Greek Document Datasets Overview
The study utilized two distinct Old Greek datasets, offering diverse linguistic and historical contexts to thoroughly evaluate the LLM-based OCR correction. These datasets represent different challenges in terms of font, layout, and language evolution.
| Feature | ShakeIT Dataset | HParliament Dataset |
|---|---|---|
| Content Type | Shakespearean drama translations | Parliamentary Questions |
| Language Style | Idiomatic, literary, early 20th-century polytonic Greek | Formal, rhetorical, modern polytonic Greek |
| Time Period (Evaluated) | 1916 (originating from 1842+) | 1974-1977 |
| Document Type | Machine-printed | Typewritten |
| OCR Model Training Data | 72,313 text line images | 37,026 text line images |
Quantify Your AI Efficiency Gains
Estimate the potential time and cost savings by implementing AI-powered OCR correction in your organization. Adjust the parameters to see a personalized ROI.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
Our structured approach ensures a smooth and effective integration of AI-powered OCR correction into your existing workflows, tailored to your specific needs.
Phase 1: Discovery & Assessment
Initial consultation to understand your historical document archives, current OCR challenges, and specific linguistic requirements. We analyze your existing data and infrastructure to propose the most suitable LLM and OCR solutions.
Phase 2: Custom Model Training & Fine-Tuning
Develop or fine-tune OCR models on your specific document types (e.g., machine-printed, typewritten, specific historical fonts) and train LLMs with domain-specific knowledge and in-context learning examples for optimal correction.
Phase 3: Integration & Deployment
Seamless integration of the AI correction pipeline into your digitization workflow. This includes API setup, batch processing, and user interface development for reviewing and managing corrected outputs.
Phase 4: Monitoring & Optimization
Continuous monitoring of system performance, ongoing model optimization based on new data, and regular reports on accuracy improvements and efficiency gains. We ensure your solution evolves with your needs.
Ready to Transform Your Historical Documents?
Unlock the full potential of your archives with AI-driven precision. Schedule a free consultation to see how our solutions can benefit your institution.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat