Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- image-classification
+language:
+- am
+- ti
+tags:
+- ocr
+- handwriting-recognition
+- ethiopic
+- geez
+- amharic
+- character-recognition
+pretty_name: Geez Handwritten Character Dataset
+size_categories:
+- 10K<n<100K
+---
+
+# Amharic (Geʽez) Handwritten Character Dataset (32×32)
+
+## Dataset Details
+
+### Description
+
+This dataset contains handwritten images of Amharic (Geʽez script) characters intended for character-level Optical Character Recognition (OCR) and handwriting recognition research.
+
+| Property | Value |
+|----------|-------|
+| **Total Images** | 13,000+ |
+| **Classes** | 287 distinct characters |
+| **Image Size** | 32 × 32 pixels |
+| **Format** | Grayscale |
+| **Distribution** | Balanced across all classes |
+
+The dataset is designed to support **CPU-efficient character classifiers** and low-resource language research, particularly for Ethiopic scripts.
+
+- **Curated by:** Yared
+- **Language:** Amharic (Geʽez / Ethiopic script)
+- **License:** Apache License 2.0
+
+---
+
+## Uses
+
+### Direct Use
+
+- Training and evaluating handwritten character classifiers
+- OCR pipelines that operate at character level
+- Research on low-resource and underrepresented scripts
+- Benchmarking lightweight CNN models on constrained hardware (CPU, low RAM)
+
+### Out-of-Scope Use
+
+- Writer identification or biometric analysis
+- Forensic handwriting attribution
+- Recognition of printed or typeset Amharic text
+- Word-level or sentence-level language modeling without additional segmentation
+
+---
+
+## Dataset Structure
+
+### Data Fields
+
+Each sample contains:
+
+| Field | Description |
+|-------|-------------|
+| `image` | 32×32 grayscale image of a single handwritten character |
+| `label` | Integer class index in range `[0, 286]` |
+
+### Directory Layout
+
+```
+dataset/
+├── train/
+│   ├── 0/
+│   ├── 1/
+│   ├── ...
+│   └── 286/
+└── test/
+    ├── 0/
+    ├── 1/
+    ├── ...
+    └── n/
+```
+
+Folder names correspond directly to character class IDs.
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+
+Publicly available datasets for handwritten Ethiopic scripts are scarce, especially at character level. This dataset was created to provide a **standardized, balanced, and lightweight benchmark** for Amharic handwritten character recognition, enabling both academic research and practical OCR system development under limited computational resources.
+
+### Source Data
+
+#### Data Collection and Processing
+
+1. Handwritten characters were collected on paper forms
+2. Pages were scanned or photographed
+3. Individual characters were extracted and cropped
+4. Images were converted to grayscale
+5. Resized to a fixed resolution of 32×32 pixels
+6. Manually organized into class-specific directories
+
+No synthetic data generation was used.
+
+#### Source Data Producers
+
+The handwritten samples were produced by human contributors mainly in an academic native environment though a portion of participants are also tigrinya native. No personally identifiable information is associated with the samples.
+
+---
+
+## Annotations
+
+### Annotation Process
+
+Annotations are implicit and directory-based. Each image inherits its label from the directory name representing a specific Geʽez character class. This mapping serves as the ground-truth annotation.
+
+### Annotators
+
+Annotation and class assignment were performed by the dataset creator during dataset organization and validation.
+
+### Personal and Sensitive Information
+
+This dataset does **not** contain:
+
+- Names or identifiers
+- Demographic metadata
+- Sensitive personal information
+
+The dataset consists solely of isolated handwritten character images.
+
+---
+
+## Bias, Risks, and Limitations
+
+| Consideration | Description |
+|---------------|-------------|
+| **Demographic bias** | Handwriting styles may reflect a limited demographic group due to localized data collection from less than 500 Dire Dawa Universty Students only |
+| **Style coverage** | Extreme handwriting variations (e.g., elderly or non-academic writers) may be underrepresented |
+| **Scope limitation** | Character-level only; does not capture word or sentence context and due to the 500 participants some unique paterns might not be collected |
+
+### Recommendations
+
+- Fine-tune models with additional local handwriting samples for deployment
+- Combine this dataset with document-level segmentation pipelines when building full OCR systems
+- Apply data augmentation to improve robustness to handwriting variability
+
+---
+
+## Citation
+
+If you use this dataset in your work, please cite it as follows:
+
+### BibTeX
+
+```bibtex
+@dataset{amharic_handwritten_characters_2024,
+  author       = {Yared},
+  title        = {Amharic (Geʽez) Handwritten Character Dataset},
+  year         = {2024},
+  publisher    = {Hugging Face},
+  license      = {Apache-2.0},
+  url          = {https://huggingface.co/datasets/Yaredoffice/geez-characters}
+}
+```
+
+### APA
+
+Yared. (2024). *Amharic (Geʽez) Handwritten Character Dataset*. Hugging Face. https://huggingface.co/datasets/Yaredoffice/geez-characters
+
+---
+
+## Dataset Card Authors
+
+**Yared**
+
+## Contact
+
+For questions or contributions, please reach out via the dataset's Hugging Face discussion tab or the author's GitHub profile.