Update README.md

README.md

@@ -1,80 +1,79 @@
 ---
 license: cc-by-4.0
-pretty_name: Street Videos Dataset
+pretty_name: Handwriting Recognition Dataset
 language:
   - en
 tags:
-  - street-videos
-  - urban
-  - video-recognition
-  - detection
-  - surveillance
+  - handwriting
+  - ocr
+  - computer-vision
+  - text-recognition
+  - ai-research
+  - handwritten-text
 task_categories:
-  - video-classification
-  - object-detection
+  - image-classification
 size_categories:
-  - 1K<n<10K
+  - n<1K
 ---
 
-# Dataset Card for Street Videos Dataset
+# Dataset Card for Handwriting Recognition Dataset
 
-This dataset contains a collection of street videos designed to support tasks such as video classification, object detection, and urban scene analysis. Each video captures various street environments, including urban settings, pedestrian areas, and traffic scenarios, to provide diverse training data for computer vision models.
+This dataset contains a collection of handwritten text images designed to improve OCR (Optical Character Recognition) and text recognition models. Each image is labeled with a transcription of the same sentence, allowing models to learn to map handwritten content to its textual equivalent.
 
 ## Dataset Details
 
 ### Dataset Description
 
-This dataset contains videos of street scenes contributed by various sources. The videos are intended to enable the development and benchmarking of models that can:
-- Classify activities or events in street scenes.
-- Detect objects such as pedestrians, vehicles, or traffic signs.
-- Support surveillance, traffic monitoring, or autonomous navigation systems.
+This dataset contains images of handwritten English text contributed by various individuals. Each image includes the same standard sentence:
 
-The dataset is ideal for training and evaluating models for urban computer vision and video analysis applications.
+> "AI learns from data. Your handwriting helps machines read text better. Write clearly; good handwriting boosts AI accuracy. This small act aids AI research. Thanks for your support!"
+
+The dataset is ideal for training and evaluating OCR models and applications involving handwritten text recognition.
 
 ## Uses
 
 ### Direct Use
 
-- Training video classification models to recognize activities in street scenes.
-- Fine-tuning object detection models for urban environments.
-- Research in autonomous navigation and smart city technologies.
+- Training OCR models to recognize English handwritten text.
+- Fine-tuning vision models on handwritten content.
+- Educational purposes in AI research and ML bootcamps.
 
 ### Out-of-Scope Use
 
-- Real-time surveillance without proper ethical considerations.
+- Real-time handwriting verification or personal identity recognition.
 - Commercial use without proper attribution under CC BY 4.0.
-- Any use that attempts to identify individuals in videos.
+- Any use that attempts to link handwriting to individuals.
 
 ## Dataset Structure
 
 Each sample consists of:
 
-- A video (`.mp4` or `.avi`) stored in the `videos/` directory.
+- An image (`.jpg` or `.png`) stored in the `images/` directory.
 - A `metadata.csv` file with columns:
-  - `file_name`: name of the video file (e.g., `video_01.mp4`)
-  - `description`: brief description of the scene or activity (if applicable)
+  - `file_name`: name of the image file (e.g., `sample_01.jpg`)
+  - `text`: transcription of the handwritten sentence (identical for all rows)
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-The dataset was curated to support research and development in urban computer vision and video analysis. The diversity in locations, times of day, and activities enhances model generalization and robustness.
+The dataset was curated to help improve handwritten text recognition, especially for machine learning systems that require structured, consistent inputs.
 
 ### Source Data
 
 #### Data Collection and Processing
 
-Videos were collected from various urban environments under different lighting and weather conditions. All videos were manually reviewed for quality and relevance.
+Contributors were asked to write a standard sentence on paper and scan or photograph it under good lighting. All images were manually checked for clarity, contrast, and legibility.
 
 #### Who are the source data producers?
 
-Anonymous contributors capturing street footage. No personal data was collected.
+Anonymous contributors with diverse handwriting styles. No personal data was collected.
 
 ### Annotations
 
 #### Annotation process
 
-Videos are paired with descriptive metadata where applicable. Annotations for specific objects or activities may be provided in separate files or require manual labeling by users.
+Each image is paired with the same predefined sentence. Since all transcriptions are identical, no manual transcription was required.
 
 #### Personal and Sensitive Information
 
@@ -82,13 +81,13 @@ No personally identifiable or sensitive data is included in the dataset.
 
 ## Bias, Risks, and Limitations
 
-- The dataset may not include all possible urban environments or rare events.
-- Low-light or extreme weather conditions may be underrepresented.
-- Models trained on this dataset may not generalize well to all real-world urban scenarios.
+- Handwriting samples may lack diversity in script style and regional variations.
+- All samples use English and the same sentence — not suitable for language modeling or multilingual OCR.
+- Models trained on this dataset may not generalize well to varied real-world handwriting.
 
 ### Recommendations
 
-- Combine with other video datasets for broader coverage.
+- Combine with other handwritten datasets for broader coverage.
 - Use only for academic, non-commercial experimentation unless explicitly licensed.
 
 ---
@@ -103,10 +102,10 @@ No personally identifiable or sensitive data is included in the dataset.
 **BibTeX:**
 
 ```bibtex
-@misc{streetvideos2025,
-  title = {Street Videos Dataset},
-  author = {Anonymous Contributors},
+@misc{handwriting_recognition_dataset,
+  title = {Handwriting Recognition Dataset},
+  author = {Various Contributors},
   year = {2025},
-  howpublished = {\url{https://huggingface.co/datasets/<your-org>/street-videos}},
+  howpublished = {\url{https://huggingface.co/datasets/your-org/handwriting-recognition}},
   note = {Dataset available under CC BY 4.0 license}
-}
+}