Update README.md

README.md

@@ -16,20 +16,44 @@ tags:
 - english
 - computer-vision
 - document-ai
-pretty_name: Synthetic English OCR Detection and Recognition 120K
+pretty_name: Synthetic English OCR Detection and Recognition 240K
 size_categories:
 - 100K<n<1M
 annotations_creators:
 - machine-generated
 language_creators:
-- found
+- machine-generated
 multilinguality:
 - monolingual
 ---
 
-# Synthetic English OCR Detection and Recognition 120K
-
-A large synthetic English OCR dataset containing **120,792 full images**, **120,792 cropped text images**, polygon-based text annotations, and transcription labels.
+# Synthetic English OCR Detection and Recognition 240K
+
+> ## 📌 Current dataset size: 240,000 paired OCR samples
+>
+> The current **v2.0 release contains exactly 240,000 detector images and
+> 240,000 matching recognition crops**.
+>
+> Each sample ID corresponds to:
+>
+> - one full image for text detection;
+> - one cropped text image for text recognition;
+> - one detector JSONL record;
+> - one recognizer JSONL record.
+>
+> Therefore, the dataset contains **240,000 aligned OCR pairs** and
+> **480,000 JPEG files in total**. The detector image and recognition crop are
+> two representations of the same OCR sample, not 480,000 independent texts.
+>
+> The repository identifier still ends in
+> `synthetic-ocr-en-det-rec-120k` because that was the name of the original
+> release. The old repository name is intentionally retained to preserve
+> existing links, citations, download scripts, and bookmarks. **The repository
+> name is legacy; the current dataset size is 240K.**
+
+A large synthetic English OCR dataset containing **240,000 full detector
+images**, **240,000 cropped text images**, polygon-based text annotations, and
+aligned transcription labels.
 
 This dataset is designed for training and evaluating:
 
@@ -41,30 +65,60 @@ This dataset is designed for training and evaluating:
 - Polygon-based text localization
 - Lightweight mobile and ONNX OCR models
 
-The dataset includes two complete and independent subsets:
+The dataset includes two aligned subsets:
 
 1. **Detector dataset** for locating text regions in full images.
 2. **Recognizer dataset** for converting cropped text images into English text.
 
+Both subsets use the same numeric sample IDs. For example,
+`images/en_00000001.jpg` and `crops/en_00000001.jpg` belong to the same OCR
+sample and share the same ground-truth transcription.
+
 ## Dataset Summary
 
 | Property | Value |
 |---|---:|
 | Language | English |
-| Full detector images | 120,792 |
-| Recognition crops | 120,792 |
-| Detector annotations | 120,792 |
-| Recognition annotations | 120,792 |
+| Paired OCR samples | 240,000 |
+| Full detector images | 240,000 |
+| Recognition crops | 240,000 |
+| Total JPEG files | 480,000 |
+| Detector annotations | 240,000 |
+| Recognition annotations | 240,000 |
 | Annotation format | JSON Lines |
 | Detection geometry | Multi-point polygons |
 | Image format | JPEG |
 | Data type | Synthetic OCR |
+| Current release | v2.0 |
 | License | CC BY-NC 4.0 |
 | Creator | Trần Phi |
 
-The images contain English text rendered using different fonts, positions, sizes, rotations, backgrounds, and visual styles.
+The images contain English text rendered using different fonts, positions,
+sizes, rotations, backgrounds, colors, and visual styles.
+
+The goal is to provide a practical OCR training resource for researchers and
+developers building text detection and recognition systems.
+
+## Repository Name Notice
 
-The goal is to provide a practical OCR training resource for researchers and developers building text detection and recognition systems.
+The Hugging Face repository URL remains:
+
+```text
+Phitran21/synthetic-ocr-en-det-rec-120k
+```
+
+The `120k` suffix refers to the original release size. The dataset was expanded
+in place to 240,000 paired samples so that existing users would not lose access
+through old links or citations.
+
+Use the following values when describing the current release:
+
+```text
+Current title: Synthetic English OCR Detection and Recognition 240K
+Current version: v2.0
+Current paired samples: 240,000
+Legacy repository slug: synthetic-ocr-en-det-rec-120k
+```
 
 ## Repository Structure
 
@@ -77,7 +131,12 @@ synthetic-ocr-en-det-rec-120k/
 │       ├── part_002.zip
 │       ├── part_003.zip
 │       ├── part_004.zip
-│       └── part_005.zip
+│       ├── part_005.zip
+│       ├── part_006.zip
+│       ├── part_007.zip
+│       ├── part_008.zip
+│       ├── part_009.zip
+│       └── part_010.zip
 │
 └── recognizer/
     ├── recognizer.jsonl
@@ -86,41 +145,49 @@ synthetic-ocr-en-det-rec-120k/
         ├── part_002.zip
         ├── part_003.zip
         ├── part_004.zip
-        └── part_005.zip
-
-The ZIP archives are divided into several approximately equal parts to make downloading, storage, verification, and extraction easier.
-
-File Descriptions
-
-detector/detector.jsonl
-
-This file contains annotations for training a text detection model.
-
-Each JSON line represents one full image and includes:
+        ├── part_005.zip
+        ├── part_006.zip
+        ├── part_007.zip
+        ├── part_008.zip
+        ├── part_009.zip
+        └── part_010.zip
+```
 
-Image path
+The ZIP archives are divided into approximately equal parts to make
+downloading, storage, verification, and extraction easier.
 
-Image width and height
+The v2.0 extension is stored in `part_006.zip` through `part_010.zip`:
 
-Text polygon coordinates
+| ZIP part | Sample ID range | Files per detector/recognizer archive |
+|---|---:|---:|
+| `part_006.zip` | 120791–144632 | 23,842 |
+| `part_007.zip` | 144633–168474 | 23,842 |
+| `part_008.zip` | 168475–192316 | 23,842 |
+| `part_009.zip` | 192317–216158 | 23,842 |
+| `part_010.zip` | 216159–240000 | 23,842 |
 
-Ground-truth transcription
+## File Descriptions
 
-Language
+### `detector/detector.jsonl`
 
-Text direction
-
-Confidence
-
-Background information
-
-Font information
+This file contains annotations for training a text detection model.
 
-Synthetic-data indicator
+Each JSON line represents one full image and includes:
 
+- Image path
+- Image width and height
+- Text polygon coordinates
+- Ground-truth transcription
+- Language
+- Text direction
+- Confidence
+- Background information
+- Font information
+- Synthetic-data indicator
 
 Example:
 
+```json
 {
   "image": "images/en_00000001.jpg",
   "width": 960,
@@ -140,44 +207,44 @@ Example:
   ],
   "synthetic": true
 }
+```
 
-The complete polygon contains multiple coordinate points that describe the text boundary more precisely than a simple rectangular bounding box.
+The complete polygon contains multiple coordinate points that describe the
+text boundary more precisely than a simple rectangular bounding box.
 
 Use this file together with:
 
+```text
 detector/images/part_*.zip
+```
 
-After extraction, the image paths should have the following structure:
+After extraction, the image paths have the following structure:
 
+```text
 images/en_00000001.jpg
 images/en_00000002.jpg
 ...
+images/en_00240000.jpg
+```
 
-recognizer/recognizer.jsonl
+### `recognizer/recognizer.jsonl`
 
 This file contains annotations for training a text recognition model.
 
 Each JSON line represents one cropped text image and includes:
 
-Crop image path
-
-Ground-truth text
-
-Language
-
-Text direction
-
-Original source image
-
-Original source polygon
-
-Crop box
-
-Rotation angle
-
+- Crop image path
+- Ground-truth text
+- Language
+- Text direction
+- Original source image
+- Original source polygon
+- Crop box
+- Rotation angle
 
 Example:
 
+```json
 {
   "image": "crops/en_00000001.jpg",
   "text": "Example English text.",
@@ -192,169 +259,215 @@ Example:
   "crop_box": [39, 92, 767, 314],
   "rotation": -3.257
 }
+```
 
 Use this file together with:
 
+```text
 recognizer/crops/part_*.zip
+```
 
-After extraction, the crop paths should have the following structure:
+After extraction, the crop paths have the following structure:
 
+```text
 crops/en_00000001.jpg
 crops/en_00000002.jpg
 ...
+crops/en_00240000.jpg
+```
 
-Detection Dataset Format
+## Pair Alignment
+
+Detector and recognizer records are aligned by their numeric IDs:
+
+```text
+Detector image:     images/en_00012345.jpg
+Recognizer crop:   crops/en_00012345.jpg
+Detector record:   detector JSONL record for ID 00012345
+Recognizer record: recognizer JSONL record for ID 00012345
+Ground-truth text: identical in both records
+```
+
+This alignment allows the two subsets to be trained independently or combined
+in an end-to-end OCR pipeline.
+
+## Detection Dataset Format
 
 The detector subset follows this relationship:
 
+```text
 Full image
     ↓
 Polygon annotation
     ↓
 Text region localization
+```
 
 Training input:
 
+```text
 images/en_XXXXXXXX.jpg
+```
 
 Training target:
 
+```text
 items[].polygon
+```
 
 Optional transcription information is available in:
 
+```text
 items[].text
+```
 
-This makes the detector annotations suitable for:
+The detector annotations are suitable for:
 
-DBNet
+- DBNet
+- Differentiable Binarization OCR
+- CRAFT-style detectors
+- EAST-style detectors
+- Segmentation-based OCR detection
+- Polygon regression models
+- Custom object detection pipelines
 
-Differentiable Binarization OCR
+Some frameworks require four-point quadrilaterals or rectangular boxes. In
+that case, polygon coordinates can be converted during preprocessing.
 
-CRAFT-style detectors
-
-EAST-style detectors
-
-Segmentation-based OCR detection
-
-Polygon regression models
-
-Custom object detection pipelines
-
-
-Some frameworks require four-point quadrilaterals or rectangular boxes. In that case, the polygon coordinates can be converted during preprocessing.
-
-Recognition Dataset Format
+## Recognition Dataset Format
 
 The recognizer subset follows this relationship:
 
+```text
 Cropped text image
     ↓
 OCR recognition model
     ↓
 English transcription
+```
 
 Training input:
 
+```text
 crops/en_XXXXXXXX.jpg
+```
 
 Training target:
 
+```text
 text
+```
 
 This subset can be used with:
 
-CRNN
-
-CTC-based OCR
-
-Transformer OCR
-
-Attention-based recognition
-
-SVTR
-
-PARSeq-style systems
-
-PaddleOCR recognition models
-
-ONNX Runtime OCR pipelines
-
-Mobile OCR applications
+- CRNN
+- CTC-based OCR
+- Transformer OCR
+- Attention-based recognition
+- SVTR
+- PARSeq-style systems
+- PaddleOCR recognition models
+- ONNX Runtime OCR pipelines
+- Mobile OCR applications
 
+## Downloading the Dataset
 
-Downloading the Dataset
-
-Using Git:
+### Using Git
 
+```bash
 git lfs install
 
 git clone \
   https://huggingface.co/datasets/Phitran21/synthetic-ocr-en-det-rec-120k
+```
 
-Using the Hugging Face CLI:
+### Using the Hugging Face CLI
 
+```bash
 pip install -U huggingface_hub
 
 hf download \
   Phitran21/synthetic-ocr-en-det-rec-120k \
   --repo-type dataset \
-  --local-dir synthetic-ocr-en-det-rec-120k
+  --local-dir synthetic-ocr-en-det-rec-240k
+```
+
+The local directory may use `240k` even though the stable remote repository
+slug still uses `120k`.
 
-Extracting the Detector Images
+## Extracting the Detector Images
 
 Linux or Ubuntu:
 
+```bash
 mkdir -p extracted_detector
 
 for file in detector/images/part_*.zip; do
   unzip "$file" -d extracted_detector
 done
+```
 
 Result:
 
+```text
 extracted_detector/
 └── images/
     ├── en_00000001.jpg
     ├── en_00000002.jpg
-    └── ...
+    ├── ...
+    └── en_00240000.jpg
+```
 
 The corresponding annotation file is:
 
+```text
 detector/detector.jsonl
+```
 
-Extracting the Recognition Crops
+## Extracting the Recognition Crops
 
+```bash
 mkdir -p extracted_recognizer
 
 for file in recognizer/crops/part_*.zip; do
   unzip "$file" -d extracted_recognizer
 done
+```
 
 Result:
 
+```text
 extracted_recognizer/
 └── crops/
     ├── en_00000001.jpg
     ├── en_00000002.jpg
-    └── ...
+    ├── ...
+    └── en_00240000.jpg
+```
 
 The corresponding annotation file is:
 
+```text
 recognizer/recognizer.jsonl
+```
 
-Quick JSONL Inspection
+## Quick JSONL Inspection
 
 Inspect the first detector samples:
 
+```bash
 head -n 5 detector/detector.jsonl
+```
 
 Inspect the first recognition samples:
 
+```bash
 head -n 5 recognizer/recognizer.jsonl
+```
 
 Validate JSONL using Python:
 
+```python
 import json
 from pathlib import Path
 
@@ -384,17 +497,25 @@ print(
     "Recognizer records:",
     validate_jsonl("recognizer/recognizer.jsonl"),
 )
+```
+
+Expected result for v2.0:
+
+```text
+Detector records: 240000
+Recognizer records: 240000
+```
 
-Python Loading Example
+## Python Loading Example
 
 Load recognition annotations:
 
+```python
 import json
 from pathlib import Path
 
 
 annotation_path = Path("recognizer/recognizer.jsonl")
-
 samples = []
 
 with annotation_path.open("r", encoding="utf-8") as file:
@@ -403,9 +524,11 @@ with annotation_path.open("r", encoding="utf-8") as file:
 
 print("Number of samples:", len(samples))
 print("First sample:", samples[0])
+```
 
 Load detector annotations:
 
+```python
 import json
 from pathlib import Path
 
@@ -420,31 +543,32 @@ print("Width:", first_sample["width"])
 print("Height:", first_sample["height"])
 print("Text items:", len(first_sample["items"]))
 print("First polygon:", first_sample["items"][0]["polygon"])
+```
 
-Suggested Train, Validation, and Test Split
+## Suggested Train, Validation, and Test Split
 
 The dataset is currently distributed as one complete collection.
 
 A recommended split is:
 
-Split	Percentage
-
-Training	90%
-Validation	5%
-Test	5%
-
+| Split | Percentage | Approximate samples |
+|---|---:|---:|
+| Training | 90% | 216,000 |
+| Validation | 5% | 12,000 |
+| Test | 5% | 12,000 |
 
-Split by filename or record index using a fixed random seed to ensure reproducibility.
+Split by filename or record index using a fixed random seed to ensure
+reproducibility.
 
 Example:
 
+```python
 import json
 import random
 from pathlib import Path
 
 
 random.seed(42)
-
 source = Path("recognizer/recognizer.jsonl")
 
 with source.open("r", encoding="utf-8") as file:
@@ -472,247 +596,226 @@ for split_name, split_rows in splits.items():
             )
 
     print(split_name, len(split_rows))
+```
 
-Intended Uses
-
-This dataset is intended for:
-
-Academic OCR research
-
-Non-commercial OCR model training
-
-OCR benchmarking
-
-Text detection experiments
+When creating detector and recognizer splits, use the same sample IDs for both
+subsets so that pair alignment is preserved.
 
-Text recognition experiments
+## Intended Uses
 
-Synthetic-data research
-
-Document AI research
-
-Mobile OCR development
-
-ONNX and ONNX Runtime experiments
-
-Educational projects
-
-Personal non-commercial projects
+This dataset is intended for:
 
+- Academic OCR research
+- Non-commercial OCR model training
+- OCR benchmarking
+- Text detection experiments
+- Text recognition experiments
+- Synthetic-data research
+- Document AI research
+- Mobile OCR development
+- ONNX and ONNX Runtime experiments
+- Educational projects
+- Personal non-commercial projects
 
-Out-of-Scope Uses
+## Out-of-Scope Uses
 
 The dataset must not be used for:
 
-Commercial use without written permission
-
-Illegal surveillance
-
-Privacy-invasive identification systems
-
-Misleading or fraudulent applications
+- Commercial use without written permission
+- Illegal surveillance
+- Privacy-invasive identification systems
+- Misleading or fraudulent applications
+- Applications that violate applicable laws
+- Claiming the dataset was manually collected or manually annotated
+- Redistributing the dataset under incompatible terms
 
-Applications that violate applicable laws
+## Limitations
 
-Claiming the dataset was manually collected or manually annotated
-
-Redistributing the dataset under incompatible terms
-
-
-Limitations
-
-This is a synthetic dataset and does not fully represent all real-world OCR conditions.
+This is a synthetic dataset and does not fully represent all real-world OCR
+conditions.
 
 Possible limitations include:
 
-Synthetic fonts and rendering patterns
-
-Limited background diversity
-
-Limited handwriting coverage
-
-Limited severe blur and compression artifacts
-
-Limited curved or highly distorted text
-
-Possible unnatural source sentences
+- Synthetic fonts and rendering patterns
+- Limited background diversity
+- Limited handwriting coverage
+- Limited severe blur and compression artifacts
+- Limited curved or highly distorted text
+- Possible unnatural source sentences
+- Possible differences from photographs taken by real cameras
+- Possible imbalance among fonts, rotations, text lengths, and backgrounds
+- Primarily horizontal English text
+- No guarantee of perfect semantic or grammatical quality in every sentence
 
-Possible differences from photographs taken by real cameras
+Models trained exclusively on this dataset may require fine-tuning on real-world
+OCR data before production use.
 
-Possible imbalance among fonts, rotations, text lengths, and backgrounds
+For stronger generalization, consider combining this dataset with legally
+compatible real-image datasets.
 
-Primarily horizontal English text
+## Data Quality Notes
 
-No guarantee of perfect semantic or grammatical quality in every sentence
+The annotations were generated automatically as part of the synthetic
+rendering process.
 
+Because the text, polygon, crop, and transcription originate from the same
+generation pipeline, labels are expected to align closely with their
+corresponding images.
 
-Models trained exclusively on this dataset may require fine-tuning on real-world OCR data before production use.
+The v2.0 release was packaged with:
 
-For stronger generalization, consider combining this dataset with legally compatible real-image datasets.
+- Aligned detector and recognizer IDs
+- Matching detector and recognizer text
+- Continuous IDs from `00000001` through `00240000`
+- JSONL syntax validation
+- Missing-file checks
+- Duplicate-ID checks
+- Duplicate-text checks for the newly generated extension
+- ZIP path and CRC verification
 
-Data Quality Notes
-
-The annotations were generated automatically as part of the synthetic rendering process.
-
-Because the text, polygon, crop, and transcription originate from the same generation pipeline, labels are expected to align closely with their corresponding images.
-
-However, users should still perform their own validation before training production systems.
+Users should still perform their own validation before training production
+systems.
 
 Recommended checks include:
 
-ZIP integrity
-
-Missing file detection
-
-Duplicate file detection
-
-JSONL parsing
-
-Image readability
-
-Polygon coordinate bounds
-
-Empty transcription detection
+- ZIP integrity
+- Missing file detection
+- Duplicate file detection
+- JSONL parsing
+- Image readability
+- Polygon coordinate bounds
+- Empty transcription detection
+- Train and test leakage detection
 
-Train and test leakage detection
-
-
-License
+## License
 
 This dataset is licensed under the:
 
-Creative Commons Attribution-NonCommercial 4.0 International License
-CC BY-NC 4.0
+**Creative Commons Attribution-NonCommercial 4.0 International License  
+CC BY-NC 4.0**
 
 You may:
 
-Use the dataset for research
-
-Use the dataset for education
-
-Use the dataset for personal projects
-
-Modify and adapt the dataset
-
-Train non-commercial models
-
-Redistribute permitted adaptations with attribution
-
+- Use the dataset for research
+- Use the dataset for education
+- Use the dataset for personal projects
+- Modify and adapt the dataset
+- Train non-commercial models
+- Redistribute permitted adaptations with attribution
 
 You must:
 
-Credit the original creator
-
-Link or refer to this dataset repository
-
-Clearly indicate significant modifications
-
-Keep attribution information visible
-
-Comply with the CC BY-NC 4.0 license
-
+- Credit the original creator
+- Link or refer to this dataset repository
+- Clearly indicate significant modifications
+- Keep attribution information visible
+- Comply with the CC BY-NC 4.0 license
 
 You may not:
 
-Use the dataset commercially without prior written permission
-
-Sell the dataset or access to the dataset
-
-Include the dataset in a paid commercial product without permission
-
-Use the dataset to provide a paid OCR service without permission
-
-Re-license the original dataset under incompatible terms
-
-Claim ownership of the original dataset
+- Use the dataset commercially without prior written permission
+- Sell the dataset or access to the dataset
+- Include the dataset in a paid commercial product without permission
+- Use the dataset to provide a paid OCR service without permission
+- Re-license the original dataset under incompatible terms
+- Claim ownership of the original dataset
 
-
-Commercial Licensing
+## Commercial Licensing
 
 Commercial use is not included under the public CC BY-NC 4.0 license.
 
-For commercial use, enterprise use, paid products, paid APIs, paid applications, commercial model training, or commercial redistribution, prior written permission is required.
+For commercial use, enterprise use, paid products, paid APIs, paid
+applications, commercial model training, or commercial redistribution, prior
+written permission is required.
 
 Please contact the creator to discuss a separate commercial license.
 
-Attribution
+## Attribution
 
 Suggested citation:
 
-Synthetic English OCR Detection and Recognition 120K
+```text
+Synthetic English OCR Detection and Recognition 240K
+Version 2.0
 Created by Trần Phi
 Hugging Face: Phitran21/synthetic-ocr-en-det-rec-120k
 License: CC BY-NC 4.0
+```
 
 Suggested attribution for model cards:
 
+```text
 This model was trained using the Synthetic English OCR Detection
-and Recognition 120K dataset created by Trần Phi:
-Phitran21/synthetic-ocr-en-det-rec-120k.
+and Recognition 240K dataset (v2.0), created by Trần Phi:
+https://huggingface.co/datasets/Phitran21/synthetic-ocr-en-det-rec-120k
+```
 
-Creator
+The repository URL contains the legacy `120k` identifier, while the current
+release contains 240,000 paired samples.
 
-Trần Phi
+## Creator
+
+**Trần Phi**
 
 Hugging Face:
 
 https://huggingface.co/Phitran21
 
-Contact
+## Contact
 
 For dataset questions, issue reports, collaboration, or commercial licensing:
 
-Email: [email protected]
-
-Facebook: https://www.facebook.com/share/1PqDzPQJYf/
+- Email: [email protected]
+- Facebook: https://www.facebook.com/share/1PqDzPQJYf/
+- Website: https://toren.io.vn
 
-Website: https://toren.io.vn
+Email responses may be limited or delayed. For public technical questions,
+using the Hugging Face Community tab is recommended.
 
-
-Email responses may be limited or delayed. For public technical questions, using the Hugging Face Community tab is recommended.
-
-Reporting Issues
+## Reporting Issues
 
 When reporting a problem, please include:
 
-Affected file name
-
-ZIP part name
-
-JSONL line number
-
-Description of the issue
-
-Minimal reproduction steps
-
-Screenshot or sample when appropriate
-
-
-Please use the Community tab of this repository for public bug reports and technical discussions.
-
-Version
-
-Current release:
-
-v1.0
-
-This release contains:
+- Affected file name
+- ZIP part name
+- JSONL line number
+- Description of the issue
+- Minimal reproduction steps
+- Screenshot or sample when appropriate
 
-120,792 detector images
+Please use the Community tab of this repository for public bug reports and
+technical discussions.
 
-120,792 recognition crops
+## Version History
 
-Detector JSONL annotations
+### v2.0 — Current
 
-Recognition JSONL annotations
+- 240,000 detector images
+- 240,000 recognition crops
+- 240,000 aligned detector/recognizer pairs
+- 480,000 JPEG files in total
+- Detector JSONL annotations
+- Recognition JSONL annotations
+- Ten detector ZIP archives
+- Ten recognizer ZIP archives
+- Continuous sample IDs through `en_00240000.jpg`
 
-Five detector ZIP archives
+### v1.0 — Legacy 120K release
 
-Five recognizer ZIP archives
+- Initial approximately 120K paired OCR release
+- Five detector ZIP archives
+- Five recognizer ZIP archives
+- Original repository name established as
+  `synthetic-ocr-en-det-rec-120k`
 
+The repository name was retained for backward compatibility when v2.0 expanded
+the dataset to 240K.
 
-Acknowledgements
+## Acknowledgements
 
-Thank you to the open-source OCR, computer vision, Python, font, and machine-learning communities whose tools and research make synthetic dataset creation possible.
+Thank you to the open-source OCR, computer vision, Python, font, and
+machine-learning communities whose tools and research make synthetic dataset
+creation possible.
 
-If this dataset is useful in your research or project, please consider giving the repository a like and citing the dataset.
+If this dataset is useful in your research or project, please consider giving
+the repository a like and citing the dataset.