Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+pretty_name: 'Korean Handwritten Notes Dataset'
+language:
+- ko
+tags:
+- image
+- handwritten
+- korean
+- notes
+- text-recognition
+- ocr
+- document-understanding
+- ai-research
+- computer-vision
+task_categories:
+- image-classification
+- text-recognition
+- handwriting-analysis
+size_categories:
+- 1K<n<10K
+---
+
+# Korean Handwritten Notes Dataset
+*This dataset contains high-resolution images of Korean handwritten notes, including personal notes, class notes, and informal writings. The dataset has been anonymized and curated to support AI research in handwriting recognition, OCR, and document understanding.*
+
+## Contact
+For queries or collaborations related to this dataset, contact:  
+  - anoushka@kgen.io  
+  - abhishek.vadapalli@kgen.io  
+
+## Supported Tasks
+
+- **Task Categories**:  
+  - Image Classification  
+  - Text Recognition (OCR)  
+  - Handwriting Analysis  
+
+- **Supported Tasks**:  
+  - Handwriting recognition of Korean text  
+  - Handwriting style classification and analysis  
+  - OCR for personal and educational handwritten documents  
+  - Document layout and structure recognition in handwritten notes  
+  - Research in AI-driven note digitization and transcription systems  
+
+## Languages
+
+- **Primary Language**: Korean  
+- **Secondary Presence**: Minimal English (if included in bilingual notes)  
+
+## Dataset Creation
+
+### Curation Rationale
+The dataset was created to facilitate AI systems that can accurately read, classify, and transcribe Korean handwriting. It is particularly useful for OCR research, education technology applications, and digitization of handwritten content.
+
+### Source Data
+- **Contributors**: Volunteer submissions, classroom notes, and simulated handwritten samples  
+- **Collection Process**: Handwritten notes were scanned or photographed. All personal identifiers, such as names or contact information, were removed prior to inclusion.  
+
+### Other Known Limitations
+- **Bias**: Urban and student-originated notes may be overrepresented  
+- **Handwriting Variability**: Differences in penmanship, ink type, and paper quality may affect OCR performance  
+- **Content Scope**: Focused on short notes; long-form essays or formal documents are underrepresented  
+
+## Intended Uses
+
+### ✅ Direct Use
+- Training OCR and handwriting recognition models for Korean  
+- Academic research in document understanding and handwriting analysis  
+- Digitization of educational or personal handwritten content  
+- Research in AI-assisted note-taking or transcription tools  
+
+### ❌ Out-of-Scope Use
+- Reconstructing personal information from submissions  
+- Commercial reuse of individual handwriting styles without consent  
+- Any surveillance or monitoring of individuals through handwritten content  
+
+## License
+
+CC BY 4.0