Datasets:

Kratos-AI
/

Handwritten-Computer-Science-Notes-Dataset

license: cc-by-4.0
pretty_name: English Handwritten Computer Science Notes Dataset
language:
  - en
tags:
  - image
  - handwritten
  - english
  - computer-science
  - notes
  - text-recognition
  - ocr
  - code-recognition
  - document-understanding
  - ai-research
  - computer-vision
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

English Handwritten Computer Science Notes Dataset

This dataset contains high-resolution images of handwritten computer science notes written in English. It includes algorithm explanations, code snippets, flowcharts, theoretical content, and annotations. The dataset is designed to support AI research in handwriting recognition, OCR, and document understanding specifically for computer science education.

Contact

For queries or collaborations related to this dataset, contact:

Supported Tasks

Task Categories:
- Image Classification
- Text and Code Recognition (OCR)
- Diagram and Flowchart Detection
- Document Layout Understanding
Supported Tasks:
- Recognition of handwritten computer science notes and pseudo-code
- OCR for structured and indented handwritten text
- Identification of algorithms, syntax patterns, and logic flow
- Detection and analysis of flowcharts or architecture diagrams
- AI research in handwritten code interpretation and educational note digitization

Languages

Primary Language: English
Secondary Presence: Programming keywords and symbols (e.g., if, for, while, { }, =, !=, etc.)

Dataset Creation

Curation Rationale

The dataset was curated to help AI systems accurately read, interpret, and digitize handwritten computer science content. It supports education, content archiving, and the development of handwriting-based programming assistance tools.

Source Data

Contributors: Students, instructors, and volunteers submitting handwritten computer science notes
Collection Process: Notes were scanned or photographed. All identifiable personal data were removed before inclusion.

Other Known Limitations

Bias: Focused mainly on academic-level notes (introductory to intermediate topics)
Variability: Differences in handwriting styles, indentation consistency, and notation use
Scope: Primarily algorithms, data structures, and theory; limited advanced research content

Intended Uses

✅ Direct Use

Training handwriting OCR and layout recognition systems
Research in handwritten programming and algorithm comprehension
Digitization of handwritten lecture and study materials
Development of AI-assisted note-to-code or note-organization tools

❌ Out-of-Scope Use

Any attempt to identify individuals by handwriting
Commercial reuse of handwriting without consent
Surveillance or behavioral analysis from handwriting or note patterns

License

CC BY 4.0