

Versatile OCR Program
Overview :
This product is a specially designed OCR system aimed at extracting structured data from complex educational materials. It supports multilingual text, mathematical formulas, tables, and charts, and can generate high-quality datasets suitable for machine learning training. The system utilizes multiple technologies and APIs to provide high-accuracy extraction results, suitable for academic research and educators.
Target Users :
This product is particularly suitable for educators, academic researchers, and users who need to process and analyze complex documents. Its high accuracy and multi-functionality allow users to generate training data more efficiently, supporting various educational and research purposes.
Use Cases
Extract mathematical problems and their diagrams from exam papers to generate training data.
Extract complex tables and figures from academic articles and generate descriptions for them.
Process illustrations and data charts in science textbooks to help students understand concepts.
Features
Multilingual Support: Compatible with Japanese, Korean, and English, with easy customization for other languages as needed.
Structured Output: Generates AI-ready output in JSON or Markdown format, including human-readable descriptions of mathematical expressions and table summaries.
High Accuracy: Achieves 90-95% accuracy on real-world academic datasets, suitable for documents with complex layouts.
Complex Layout Support: Accurately handles exam-style PDFs with dense scientific content, supporting formula-heavy paragraphs and rich visual elements.
Intelligent Interpretation: Extracted elements such as charts, tables, and figures are provided with semantic annotations and contextual explanations.
Image and Special Region Processing: Processes image regions using Google Vision API's image analysis capabilities and generates image descriptions.
Table Processing Optimization: Uses DocLayout-YOLO for table region detection, preserving table structure.
Educational Value: Helps students intuitively understand complex scientific and mathematical concepts, suitable for use in education.
How to Use
Step 1: Run ocr_stage1.py to extract raw elements (text, tables, figures, etc.) from the input PDF.
Step 2: Process the intermediate data using ocr_stage2.py to convert it into structured, human-readable output.
Step 3: Customize the output format (JSON or Markdown) as needed to adapt to machine learning requirements.
Step 4: Validate and adjust the extracted data to ensure its accuracy and completeness.
Step 5: Apply the processed data to machine learning model training or educational material development.
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