Update README.md

README.md

@@ -11,18 +11,21 @@ tags:
 - fiftyone
 - image
 - image-classification
-dataset_summary: '
+- ocr
+- visual-document-retrieval
+dataset_summary: >
 
 
 
 
-  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3482 samples.
+  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3482
+  samples.
 
 
   ## Installation
 
 
-  If you haven''t already, install FiftyOne:
+  If you haven't already, install FiftyOne:
 
 
   ```bash
@@ -44,9 +47,10 @@ dataset_summary: '
 
   # Load the dataset
 
-  # Note: other available arguments include ''max_samples'', etc
+  # Note: other available arguments include 'max_samples', etc
 
-  dataset = load_from_hub("harpreetsahota/scanned-images-dataset-for-ocr-and-vlm-finetuning")
+  dataset =
+  load_from_hub("Voxel51/scanned-images-dataset-for-ocr-and-vlm-finetuning")
 
 
   # Launch the App
@@ -54,19 +58,16 @@ dataset_summary: '
   session = fo.launch_app(dataset)
 
   ```
-
-  '
+license: mit
 ---
 
 # Dataset Card for scanned_images_dataset
 
-<!-- Provide a quick summary of the dataset. -->
-
-
+![image/png](scanned_documents.gif)
 
 
 
-This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 3482 samples.
+This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset containing **3,482 scanned document images** across 10 diverse document categories. Designed for OCR training and Vision-Language Model (VLM) fine-tuning, this dataset features real-world scanned documents with varied layouts, scanning quality, and document types.
 
 ## Installation
 
@@ -84,7 +85,7 @@ from fiftyone.utils.huggingface import load_from_hub
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/scanned-images-dataset-for-ocr-and-vlm-finetuning")
+dataset = load_from_hub("Voxel51/scanned-images-dataset-for-ocr-and-vlm-finetuning")
 
 # Launch the App
 session = fo.launch_app(dataset)
@@ -95,130 +96,228 @@ session = fo.launch_app(dataset)
 
 ### Dataset Description
 
-<!-- Provide a longer summary of what this dataset is. -->
-
-
+This dataset is a curated collection of scanned document images representing 10 common document types found in office, academic, and professional settings. The images exhibit real-world characteristics including varied scanning quality, different layouts, mixed content types (text, tables, images), and both printed and handwritten text.
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
+The dataset is particularly valuable for:
+- Training OCR models on diverse document layouts
+- Fine-tuning Vision-Language Models for document understanding
+- Developing document classification systems
+- Testing robustness to real-world scanning artifacts and quality variations
 
-### Dataset Sources [optional]
+- **Original Source:** [Kaggle - Scanned Images Dataset for OCR and VLM finetuning](https://www.kaggle.com/datasets/suvroo/scanned-images-dataset-for-ocr-and-vlm-finetuning)
+- **Curated by:** suvroo (original), Harpreet Sahota (FiftyOne port)
+- **Language(s):** English (primary), with potential multilingual content
+- **License:** Original license from Kaggle source
 
-<!-- Provide the basic links for the dataset. -->
+### Dataset Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Original Repository:** [Kaggle Dataset](https://www.kaggle.com/datasets/suvroo/scanned-images-dataset-for-ocr-and-vlm-finetuning)
+- **Dataset Download:** [GTS.AI Mirror](https://gts.ai/dataset-download/scanned-images-dataset-for-ocr-and-vlm-finetuning/)
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
-
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
+This dataset is suitable for:
 
-[More Information Needed]
+- **OCR Model Training**: Train text extraction on real-world scanned documents with varied quality
+- **Document Classification**: Develop models to classify documents by type (email, memo, form, etc.)
+- **Vision-Language Model Fine-tuning**: Adapt VLMs for document understanding tasks
+- **Layout Analysis**: Detect and understand document structure (headers, paragraphs, tables)
+- **Document Quality Assessment**: Train models to handle various scanning qualities
+- **Multi-column Text Recognition**: Practice on complex layouts (news articles, scientific papers)
+- **Form Understanding**: Extract structured information from forms and tables
+- **Handwriting Recognition**: Some documents contain handwritten elements
+- **Real-world OCR Benchmarking**: Evaluate model performance on authentic scanned documents
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+- **Privacy-Sensitive Applications**: Documents may contain personally identifiable information
+- **Production Document Processing**: Without text annotations, this dataset is primarily for classification and pre-training
+- **Language-Specific OCR**: While primarily English, multilingual content may be present but not systematically labeled
+- **High-Precision Applications**: Scanning artifacts and quality variations may limit use in applications requiring perfect text extraction
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+### FiftyOne Schema
+
+Each sample in the dataset has the following structure:
+
+```python
+{
+    'id': str,                      # Unique sample ID
+    'media_type': 'image',          # Always 'image'
+    'filepath': str,                # Absolute path to document image
+    'tags': [],                     # Empty list (no tags)
+    'metadata': {                   # Image metadata
+        'size_bytes': int,          # File size (typically 200KB-800KB)
+        'mime_type': 'image/jpeg',  # MIME type
+        'width': int,               # Image width (varies, typically 1200-2500px)
+        'height': int,              # Image height (varies, typically 1500-3200px)
+        'num_channels': 1,          # Grayscale (1 channel)
+    },
+    'ground_truth': {               # Document classification label
+        'id': str,                  # Label ID
+        'tags': [],                 # Empty list
+        'label': str,               # Document category (see below)
+        'confidence': None,         # Not applicable
+        'logits': None,             # Not applicable
+    }
+}
+```
 
-[More Information Needed]
+### Document Categories
+
+The dataset contains 10 document categories with the following distribution:
+
+| Category | Count | Description |
+|----------|-------|-------------|
+| **Email** | 599 | Email correspondence and communications |
+| **Memo** | 620 | Internal memorandums and office notes |
+| **Letter** | 567 | Formal and informal letters |
+| **Form** | 431 | Forms, applications, and structured documents |
+| **Report** | 265 | Business and technical reports |
+| **Scientific** | 261 | Scientific papers and research documents |
+| **ADVE** (Advertisement) | 230 | Advertisements and promotional materials |
+| **Note** | 201 | Handwritten and typed notes |
+| **News** | 188 | News articles and clippings |
+| **Resume** | 120 | Résumés and CVs |
+| **Total** | **3,482** | |
+
+### Image Characteristics
+
+- **Format**: JPEG
+- **Color Mode**: Grayscale (1 channel)
+- **Resolution**: Varies (typically 72 DPI)
+- **Typical Dimensions**: 1200-2500 × 1500-3200 pixels
+- **File Size**: 200KB - 800KB per image
+- **Orientation**: Mostly portrait, some landscape
+
+### Document Features
+
+The scanned documents exhibit:
+- **Multi-column layouts** (news articles, scientific papers)
+- **Tables and forms** with structured data
+- **Mixed content** (text, images, logos)
+- **Varied text styles** (printed, handwritten, typewritten)
+- **Real scanning artifacts** (skew, noise, shadows, folds)
+- **Different scanning qualities** (low to high resolution)
+- **Multiple fonts and sizes**
+- **Headers, footers, and page numbers**
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
+This dataset was created to provide a diverse collection of real-world scanned documents for training and evaluating OCR systems and Vision-Language Models. The variety in document types, layouts, and scanning quality makes it valuable for developing robust document understanding systems that can handle real-world conditions.
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
+The dataset consists of scanned images from various document types commonly found in professional and academic settings. Documents were scanned at varying quality levels to represent real-world scenarios.
 
-### Annotations [optional]
+This FiftyOne port was created by:
+1. Downloading the original dataset from Kaggle
+2. Organizing images into 10 category directories
+3. Using FiftyOne's `ImageClassificationDirectoryTree` format to import with automatic label assignment
+4. Computing metadata for all images (dimensions, file size, color channels)
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+The directory structure maps directly to classification labels:
+```
+scanned-images-dataset-for-ocr-and-vlm-finetuning/
+├── ADVE/          (230 images)
+├── Email/         (599 images)
+├── Form/          (431 images)
+├── Letter/        (567 images)
+├── Memo/          (620 images)
+├── News/          (188 images)
+├── Note/          (201 images)
+├── Report/        (265 images)
+├── Resume/        (120 images)
+└── Scientific/    (261 images)
+```
 
-#### Annotation process
+#### Who are the source data producers?
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+The documents appear to be collected from various sources representing typical office, business, and academic materials. Specific provenance information is not available from the original dataset.
 
-[More Information Needed]
+### Annotations
 
-#### Who are the annotators?
+#### Annotation Process
 
-<!-- This section describes the people or systems who created the annotations. -->
+The dataset includes **document type classification labels** derived from the directory structure. Each image is labeled with one of 10 document categories based on its location in the folder hierarchy.
 
-[More Information Needed]
+**No text-level annotations** (OCR transcriptions, bounding boxes, or entity labels) are included. The dataset is designed for:
+- Document classification tasks
+- Pre-training OCR and VLM models
+- Layout analysis research
+- Unsupervised or self-supervised learning
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+⚠️ **Privacy Warning**: This dataset contains scanned documents that may include:
+- Personal names and addresses
+- Email addresses and contact information
+- Company names and business information
+- Academic affiliations
+- Potentially sensitive correspondence
 
-[More Information Needed]
+Users should:
+- Review documents before use in production systems
+- Implement appropriate privacy safeguards
+- Consider anonymization or redaction for sensitive applications
+- Comply with relevant privacy regulations (GDPR, CCPA, etc.)
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Known Limitations
 
-[More Information Needed]
+- **No Text Annotations**: Dataset lacks OCR transcriptions, making it unsuitable for supervised text extraction training without additional annotation
+- **Language Bias**: Primarily English documents; limited representation of other languages
+- **Domain Coverage**: May not represent all document types (e.g., forms from specific industries)
+- **Quality Variation**: Scanning quality varies significantly, which could impact model training if not handled properly
+- **Imbalanced Categories**: Category sizes range from 120 (Resume) to 620 (Memo) samples
+- **Privacy Concerns**: Contains potentially identifiable information without systematic anonymization
+- **Temporal Bias**: Document styles and formats may reflect specific time periods
+- **Geographic Bias**: Document formats may be specific to certain regions
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
+- **Use for Classification or Pre-training**: Best suited for document type classification or unsupervised pre-training
+- **Combine with Annotated Datasets**: For OCR training, combine with datasets that include text annotations
+- **Privacy Review**: Audit documents for sensitive information before deployment
+- **Augment for Balance**: Apply data augmentation or resampling to address class imbalance
+- **Quality Filtering**: Consider filtering or stratifying by scan quality for specific applications
+- **Validate on Target Domain**: Test on your specific document types before production use
+- **Consider Licensing**: Verify usage rights for your specific application
+
+## Citation
+
+If you use this dataset, please cite:
+
+**Original Dataset:**
+```bibtex
+@misc{scanned-images-ocr-vlm,
+  author = {suvroo},
+  title = {Scanned Images Dataset for OCR and VLM finetuning},
+  year = {2024},
+  publisher = {Kaggle},
+  url = {https://www.kaggle.com/datasets/suvroo/scanned-images-dataset-for-ocr-and-vlm-finetuning}
+}
+```
 
-[More Information Needed]
+**FiftyOne Port:**
+```bibtex
+@misc{scanned-images-ocr-vlm-fiftyone,
+  author = {Harpreet Sahota},
+  title = {Scanned Images Dataset for OCR and VLM finetuning (FiftyOne)},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/voxel51/scanned-images-dataset-for-ocr-and-vlm-finetuning}
+}
+```
 
 ## Dataset Card Contact
 
-[More Information Needed]
+For questions or issues with this FiftyOne port, please open an issue on the Hugging Face dataset repository.