README.md

---
license: cc-by-4.0
task_categories:
- image-classification
language:
- en
pretty_name: >-
  Mobile-Scanned Handwritten Examination Answer Scripts Image Classification
  Dataset
size_categories:
- 1K<n<10K
---
<h1 align="center" class="big-title">
  <span style="color:green; font-size:40px;">🧑‍🎓✏️📚 Student Handwritten Exam Dataset🧑‍🎓✏️📚 </span><br>
</h1>
<img src="https://huggingface.co/datasets/chandrabhuma/AnswerScripts/resolve/main/BECLogo.jpg" 
     alt="LOGO" width="150" 
     style="display:block; margin-left:auto; margin-right:auto;"/>


## 📌 Dataset Summary

This dataset contains **real-world handwritten examination answer scripts** collected from **21 students**, scanned using **mobile phone cameras**. The images were manually organized into **21 class folders**, corresponding to individual students.  

The dataset includes **5,914 images**, all resized to **224×224 pixels**, and contains **natural variations such as rotation and flipping**, making it suitable for training **robust document image classification and recognition models**.

This dataset is intended for research and development in:

✍️ Handwriting-Based Biometric Identification

🎯 Writer Identification

🤖 Image Classification


---

## 📊 Dataset Statistics

| Property | Value |
|-----------|--------|
| Total Students (Classes) | 21 |
| Total Images | 5,914 |
| Image Resolution | 224 × 224 |
| Image Type | RGB |
| Acquisition Method | Mobile Phone Scanning |
| Task Type | Image Classification |
| Dataset Structure | Folder-wise class labels |

---

## 📁 Dataset Structure


Each folder corresponds to **one student**, and contains scanned handwritten answer script images belonging to that student.

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## 🖼️ Nature of  Images

> The dataset includes diverse handwriting styles, variations in lighting, orientation, and background, providing realistic challenges for machine learning models.

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## 🎯 Intended Use

This dataset is suitable for:

- Handwritten document image classification  
- Student-wise handwriting identification  
- Document image preprocessing research  
- OCR pipeline development  
- Deep learning model benchmarking  

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## ⚠️ Limitations

- Dataset size is moderate (≈6K images).
- Images were captured using mobile devices, so lighting and perspective variations exist.
- No word-level or character-level annotations are provided.

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## 🧾 License

This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

You are free to:

- Share  
- Adapt  
- Use commercially  

**with proper attribution.**

---

## 👥 Contributors

- **Chandra Mohan Bhuma** (Professor, Dept of ECE, Bapatla Engineering College, Bapatla)
- — 📧 chandrabhuma@gmail.com  
- **Mahaboob Subani Shaik**(Asst.Professor, Dept of ECE, Bapatla Engineering College, Bapatla)
- — 📧 skmsubanibec@gmail.com  
- **Gundreddy Dhatri**(UG Student, Dept of ECE, Bapatla Engineering College, Bapatla)
-  — 📧 gundreddydhatri@gmail.com  
- **Munagala Gowtham**(UG Student, Dept of ECE, Bapatla Engineering College, Bapatla)
- — 📧 munagalagowtham130@gmail.com  
- **Motupalli Dinesh Kumar**(UG Student, Dept of ECE, Bapatla Engineering College, Bapatla)
-  — 📧 dineshmotupalli44@gmail.com  
- **Molathoti David Living Stone**(UG Student, Dept of ECE, Bapatla Engineering College, Bapatla)
-  — 📧 davidlivingstone200308@gmail.com  

---

## 📣 Citation

If you use this dataset in your research, please cite as:

@dataset{handwritten_exam_scripts_2026,
title = {Student Handwritten Exam Dataset},
author = {Chandra Mohan B, Subani, Sk. M. and Dhatri, Gundreddy and Gowtham, Munagala and Dinesh Kumar, Motupalli and David Living Stone, Molathoti},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/
<your-username>/<dataset-name>}
}