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English Handwritten Math Notes Dataset
This dataset contains high-resolution images of handwritten mathematical notes written in English. It includes problem statements, worked examples, formulas, and annotated derivations. The dataset supports AI research in handwriting recognition, mathematical OCR, and document understanding for STEM applications.
Contact
For queries or collaborations related to this dataset, contact:
Supported Tasks
Task Categories:
- Image Classification
- Text and Equation Recognition (OCR)
- Document Structure Analysis
Supported Tasks:
- Handwriting recognition of English mathematical text and symbols
- Equation segmentation and symbol-level classification
- OCR for handwritten problem-solving steps
- Document layout analysis for structured handwritten notes
- Research in AI-assisted math transcription and digital learning tools
Languages
- Primary Language: English
- Secondary Presence: Mathematical symbols, formula notation (e.g., ∑, ∫, √, etc.)
Dataset Creation
Curation Rationale
The dataset was created to advance AI models capable of interpreting handwritten mathematics and textual content together. It aids research in educational AI, note digitization, and intelligent tutoring systems.
Source Data
- Contributors: Students, educators, and volunteers who shared personal or class notes
- Collection Process: Notes were photographed or scanned. Any personal information, including names or contact details, was removed prior to inclusion.
Other Known Limitations
- Bias: Notes from specific academic levels (high school or undergraduate) may dominate
- Complexity Range: May vary from basic arithmetic to advanced calculus
- Paper and Pen Variability: Image clarity may differ based on paper type and ink used
Intended Uses
✅ Direct Use
- Training OCR and equation recognition models
- AI research in handwriting interpretation for STEM education
- Developing intelligent tutoring and digital note systems
- Enhancing document understanding for handwritten math content
❌ Out-of-Scope Use
- Reconstruction of contributor identities or personal data
- Unauthorized commercial reuse of handwriting samples
- Surveillance or profiling based on handwriting characteristics
License
CC BY 4.0
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