Update README.md

README.md

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 ---
 license: cc-by-4.0
+pretty_name: Multilingual OCR Annotation Dataset
+language:
+  - he
+  - th
+  - ja
+  - ar
+tags:
+  - multilingual-ocr
+  - ocr-annotation
+  - text-detection
+  - text-recognition
+  - document-ai
+  - computer-vision
+  - multilingual-datasets
+  - data-annotation
+size_categories:
+  - n<1K
 ---
+
+# Multilingual OCR Annotation Dataset
+
+**Publisher: DataCluster Labs**
+
+## 1. Overview
+
+The Multilingual OCR Annotation Dataset is a collection of annotated text data designed to support research and development in Optical Character Recognition (OCR), multilingual text recognition, and document intelligence.
+
+The dataset covers multiple languages and writing systems, including **Hebrew, Thai, Japanese, and Arabic**, providing diverse linguistic representations for exploring OCR capabilities across different scripts.
+
+It aims to support the development and evaluation of AI systems capable of detecting, recognizing, and extracting text from multilingual visual content.
+
+## 2. Dataset Details
+
+| Attribute | Description |
+|---|---|
+| Dataset Name | Multilingual OCR Annotation Dataset |
+| Publisher | DataCluster Labs |
+| Languages | Hebrew, Thai, Japanese, Arabic |
+| Data Category | OCR and Text Annotation |
+| Primary Domain | Computer Vision and Document AI |
+| Intended Use | AI research, model training and evaluation |
+| Release Type | Dataset release |
+
+## 3. Language Coverage
+
+The dataset includes annotations across the following languages:
+
+| Language | Script | Language Code |
+|---|---|---|
+| Hebrew | Hebrew | he |
+| Thai | Thai | th |
+| Japanese | Japanese | ja |
+| Arabic | Arabic | ar |
+
+These languages introduce different text-processing challenges, including right-to-left writing, complex character structures, script-specific segmentation, and varying word-boundary conventions.
+
+## 4. Annotation and Data Characteristics
+
+The dataset focuses on annotated textual information for OCR and computer vision applications.
+
+Depending on the annotation format, the data may include:
+
+- **Text Transcription:** Recognized textual content.
+- **Text Localization:** Position of text within an image or document.
+- **Bounding Boxes or Polygons:** Geometric representation of text regions.
+- **Language Identification:** Association of samples with their respective languages.
+- **Structured Annotations:** Machine-readable representations of OCR labels.
+
+The exact annotation fields and structures depend on the individual dataset release.
+
+## 5. Potential Applications
+
+The dataset can support research and experimentation in:
+
+- **Multilingual OCR:** Recognizing text across different languages and scripts.
+- **Text Detection:** Identifying and locating text regions in images.
+- **Text Recognition:** Converting image-based text into machine-readable content.
+- **Document Intelligence:** Extracting and understanding information from documents.
+- **Computer Vision:** Developing models for visual text understanding.
+- **Multilingual AI:** Improving language-aware visual processing systems.
+- **OCR Benchmarking:** Comparing OCR model performance across different scripts.
+
+## 6. Research Significance
+
+Multilingual OCR presents challenges because writing systems differ in character structure, directionality, spacing, typography, and segmentation requirements.
+
+For example:
+- Arabic and Hebrew use right-to-left writing systems.
+- Thai features complex character combinations and spacing conventions.
+- Japanese combines multiple writing systems, including Hiragana, Katakana, and Kanji.
+
+A diverse annotated dataset can help researchers investigate these challenges and develop more robust OCR systems across different languages.
+
+## 7. Intended Audience
+
+This dataset is intended for:
+
+- AI and machine learning researchers
+- OCR model developers
+- Computer vision engineers
+- Document AI researchers
+- Multilingual NLP practitioners
+- Data scientists and annotation teams
+- Academic institutions and research organizations
+
+## 8. Custom OCR Annotation Services
+
+Alongside dataset development, **DataCluster Labs provides custom OCR annotation and dataset creation services** for organizations, researchers, and AI teams.
+
+Our services include:
+
+- Multilingual OCR annotation
+- Text detection and transcription
+- Bounding box and polygon annotation
+- Language-specific text annotation
+- Custom OCR dataset creation
+- Dataset structuring and quality checks
+
+We can work with your existing image and document collections or create custom OCR datasets based on your project requirements.
+
+## 9. License and Usage
+
+Please refer to the license specified for this dataset release before downloading, modifying, redistributing, or using the data for commercial purposes.
+
+The applicable license and usage permissions should be confirmed for each release.
+
+## 10. Citation
+
+Please use the following citation when referencing this dataset:
+
+DataCluster Labs. *Multilingual OCR Annotation Dataset*.
+
+For academic publications and technical reports, include the Hugging Face repository URL and the date of access.
+
+## 11. Publisher and Contact
+
+**DataCluster Labs**
+
+Website: https://datacluster.ai/
+
+Email: sales@datacluster.ai/
+
+For dataset access, custom OCR annotation requirements, research collaborations, and dataset development enquiries, please contact our team.