Handwritten Equation Recognition for Education

Project Overview:

Objective

The “Handwritten Equation Recognition for Education” project aims to create a dataset for training machine learning models to accurately recognize and transcribe handwritten mathematical equations. This dataset will be instrumental in developing tools and applications to assist students and educators in digitizing and understanding handwritten mathematical notations.

Scope

This project involves collecting handwritten mathematical equations from various sources, including educational institutions, students, and publicly available datasets, and annotating them with correct digital representations.

Sources

Educational Institutions: Collaborate with educational institutions to collect handwritten equations from assignments, exams, and lecture notes.
Students and Contributors: Encourage students and contributors to submit their handwritten equations for recognition and transcription.
Public Datasets: Access publicly available datasets containing handwritten mathematical equations.

Data Collection Metrics

Total Handwritten Equations: 30,000 equations
Educational Institutions: 15,000
Students and Contributors: 10,000
Public Datasets: 5,000

Annotation Process

Stages

Equation Recognition: Annotate each handwritten equation with the corresponding digital representation, ensuring accuracy in transcribing mathematical symbols, operators, and variables.
Metadata Logging: Log metadata, including the source of the equation, the author (if available), and the context in which it was written.

Annotation Metrics

Handwritten Equations with Digital Transcriptions: 30,000
Metadata Logging: 30,000

Quality Assurance

Annotation Verification: Implement a validation process involving mathematics educators to review and verify the accuracy of equation transcriptions.
Data Quality Control: Ensure the removal of poorly written or illegible equations from the dataset.
Data Security: Protect sensitive information, maintain anonymity of contributors, and ensure data privacy.

QA Metrics

Annotation Validation Cases: 3,000 (10% of total)
Data Cleansing: Remove poorly written or illegible equations

Conclusion

The “Handwritten Equation Recognition for Education” dataset is a valuable resource for the development of educational tools and applications aimed at assisting students and educators in working with handwritten mathematical notations. With a diverse collection of accurately transcribed equations, along with comprehensive metadata, this dataset empowers the creation of advanced equation recognition models that can benefit mathematics education by automating the process of digitizing and interpreting handwritten math equations. It contributes to the enhancement of learning and teaching experiences in the field of mathematics and related disciplines.