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 entails the collection of handwritten mathematical equations from various sources, such as educational institutions, students, and publicly available datasets. Subsequently, these equations will be annotated with their correct digital representations.
Sources
- Educational Institutions: In collaboration with educational institutions, our objective is to systematically gather handwritten equations from assignments, exams, and lecture notes. By doing so, we can comprehensively analyze and understand various mathematical and scientific concepts. Additionally, this initiative will facilitate the creation of a valuable database for research and educational purposes.
- Students and Contributors: Additionally, we encourage students and contributors to submit their handwritten equations for recognition and transcription.
- Public Datasets: Furthermore, we seek to 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: The process involves annotating each handwritten equation with the corresponding digital representation, ensuring accuracy in transcribing mathematical symbols, operators, and variables.
- Metadata Logging: Moreover, it’s essential to 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
Stages
Annotation Verification: In order to implement a validation process involving mathematics educators to review and verify the accuracy of equation transcriptions, it is crucial to ensure the reliability of the data.
Data Quality Control: Additionally, it is imperative to ensure the removal of poorly written or illegible equations from the dataset to maintain its integrity and usefulness.
Data Security: Furthermore, it is essential to prioritize data security by protecting sensitive information, maintaining anonymity of contributors, and ensuring data privacy for all involved parties.
QA Metrics
- Annotation Validation Cases: 3,000 (10% of total)
- Data Cleansing: Remove poorly written or illegible equations
Conclusion
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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