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Datasets:
Arturito1
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OCR-Synthetic-Multilingual-v1

Tasks:
Object Detection
Image-to-Text
Languages:
English
Japanese
Korean
Size:
10M<n<100M
Tags:
ocr
text-detection
text-recognition
synthetic-data
synthdog
hdf5
License:
Dataset card Data Studio Files
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Arturito1 emelryan commited on
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Duplicate from nvidia/OCR-Synthetic-Multilingual-v1

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Co-authored-by: Ryan Chesler <emelryan@users.noreply.huggingface.co>

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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ # Image files - uncompressed
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+ # Image files - compressed
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - object-detection
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+ - image-to-text
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+ language:
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+ - en
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+ - ja
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+ - ko
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+ - ru
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+ - zh
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+ tags:
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+ - ocr
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+ - text-detection
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+ - text-recognition
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+ - synthetic-data
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+ - synthdog
18
+ - hdf5
19
+ - nvidia
20
+ - nemotron
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+ pretty_name: OCR Synthetic Multilingual v1
22
+ size_categories:
23
+ - 10M<n<100M
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+ ---
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+
26
+ # OCR-Synthetic-Multilingual-v1
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+
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+ ## Overview
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+
30
+ Large-scale synthetically generated OCR training dataset for multilingual text detection and recognition. The data was produced using a heavily modified and extended version of [SynthDoG](https://github.com/clovaai/donut/tree/master/synthdog) (Synthetic Document Generator), originally introduced in the [Donut](https://github.com/clovaai/donut) project by Kim et al.
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+
32
+ This dataset was used to train [**Nemotron OCR v2**](https://huggingface.co/nvidia/nemotron-ocr-v2), a state-of-the-art multilingual OCR model that is part of the [NVIDIA NeMo Retriever](https://developer.nvidia.com/nemo-retriever/) collection.
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+
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+ ## Languages
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+
36
+ | Subfolder | Language | Total Samples | Train | Test | Validation |
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+ |-------------|------------------------|---------------|----------------|----------------|----------------|
38
+ | `en` | English | 1,825,089 | 1,460,304 (63) | 183,629 (63) | 181,156 (63) |
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+ | `ja` | Japanese | 1,889,137 | 1,502,712 (67) | 193,779 (67) | 192,646 (67) |
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+ | `ko` | Korean | 2,269,540 | 1,814,994 (78) | 227,091 (78) | 227,455 (78) |
41
+ | `ru` | Russian | 1,724,733 | 1,380,404 (59) | 171,678 (59) | 172,651 (59) |
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+ | `zh_hans` | Chinese (Simplified) | 2,335,343 | 1,914,948 (83) | 210,143 (73) | 210,252 (73) |
43
+ | `zh_hant` | Chinese (Traditional) | 2,214,304 | 1,772,280 (77) | 221,867 (77) | 220,157 (77) |
44
+ | **Total** | | **12,258,146** | **9,845,642** | **1,208,187** | **1,204,317** |
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+
46
+ > Numbers in parentheses are the number of `.h5` files per split.
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+
48
+ ## Related Model
49
+
50
+ This dataset was created to train the detection, recognition, and relational components of [**Nemotron OCR v2**](https://huggingface.co/nvidia/nemotron-ocr-v2). See the model card for architecture details, evaluation results, and usage instructions.
51
+
52
+ ## Directory Layout
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+
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+ ```
55
+ OCR-Synthetic-Multilingual-v1/
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+ ├── en/
57
+ │ ├── train/
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+ │ │ ├── train_000.h5
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+ │ │ ├── train_001.h5
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+ │ │ └── ...
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+ │ ├── test/
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+ │ │ └── ...
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+ │ └── validation/
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+ │ └── ...
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+ ├── ja/
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+ │ └── ...
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+ ├── ko/
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+ │ └── ...
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+ ├── ru/
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+ │ └── ...
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+ ├── zh_hans/
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+ │ └── ...
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+ └── zh_hant/
74
+ └── ...
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+ ```
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+
77
+ ## Format — HDF5
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+
79
+ Each `.h5` file contains the following datasets (HDF5 terminology):
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+
81
+ | Key | Type | Description |
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+ |---------------|-------------------------------|-------------------------------------------------------|
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+ | `images` | object (variable-length bytes)| JPEG-encoded image bytes, one entry per sample |
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+ | `annotations` | object (variable-length str) | JSON string per sample containing bounding-box annotations |
85
+ | `dimensions` | int array `[H, W]` | Original image dimensions |
86
+ | `labels` | object (string) | Full-page text label |
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+ | `qualities` | int | JPEG quality used during encoding (typically 100) |
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+ | `sample_ids` | int | Unique sample identifier |
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+
90
+ ## Annotation JSON Schema
91
+
92
+ Each entry in `annotations` is a JSON object:
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+
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+ ```json
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+ {
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+ "word_bboxes": [
97
+ {
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+ "text": "example word or phrase",
99
+ "bbox": [x, y, w, h],
100
+ "quad": [[x0,y0], [x1,y1], [x2,y2], [x3,y3]]
101
+ }
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+ ],
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+ "line_bboxes": [
104
+ {
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+ "text": "full line of text",
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+ "bbox": [x, y, w, h],
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+ "quad": [[x0,y0], [x1,y1], [x2,y2], [x3,y3]],
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+ "para_idx": 0,
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+ "line_idx": 0,
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+ "word_indices": [0, 1, 2]
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+ }
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+ ],
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+ "para_bboxes": [...],
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+ "relation_graph": [
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+ [[0], [1], [2]],
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+ [[3], [4]]
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+ ]
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+ }
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+ ```
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+
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+ ### Bounding Box Levels
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+
123
+ - **`word_bboxes`** — One entry per word/phrase rendered as a single unit. Each contains `text`, an axis-aligned `bbox [x, y, w, h]`, and a 4-point `quad`.
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+ - **`line_bboxes`** — One entry per text line. Includes all `word_bboxes` fields plus `para_idx` (paragraph index), `line_idx` (line index within the paragraph), and `word_indices` (indices into `word_bboxes` that compose this line).
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+ - **`para_bboxes`** — One entry per paragraph bounding box.
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+ - **`relation_graph`** — Nested list encoding reading order: `relation_graph[para][sentence]` gives a list of word/line indices belonging to that sentence within the paragraph.
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+
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+ ### Quad Vertex Convention
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+
130
+ Quads are 4-point polygons stored as `[[x0,y0], [x1,y1], [x2,y2], [x3,y3]]` in clockwise order:
131
+
132
+ ```
133
+ v0 -------- v1
134
+ | |
135
+ v3 -------- v2
136
+ ```
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+
138
+ ## Loading Example
139
+
140
+ ```python
141
+ import h5py, io, json
142
+ from PIL import Image
143
+
144
+ with h5py.File("en/train/train_000.h5", "r") as f:
145
+ img_bytes = f["images"][0]
146
+ image = Image.open(io.BytesIO(img_bytes.tobytes())).convert("RGB")
147
+
148
+ annotation = json.loads(f["annotations"][0])
149
+ for line in annotation["line_bboxes"]:
150
+ print(line["text"], line["quad"])
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+ ```
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+
153
+ ---
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+
155
+ ## Per-Language Details
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+
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+ ### English (`en`)
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+
159
+ | Property | Value |
160
+ |----------------|--------------------------------------|
161
+ | Language | English (`en`) |
162
+ | Total Samples | 1,825,089 |
163
+ | Train | 1,460,304 samples (63 files) |
164
+ | Test | 183,629 samples (63 files) |
165
+ | Validation | 181,156 samples (63 files) |
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+
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+ ### Japanese (`ja`)
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+
169
+ | Property | Value |
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+ |----------------|--------------------------------------|
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+ | Language | Japanese (`ja`) |
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+ | Total Samples | 1,889,137 |
173
+ | Train | 1,502,712 samples (67 files) |
174
+ | Test | 193,779 samples (67 files) |
175
+ | Validation | 192,646 samples (67 files) |
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+
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+ ### Korean (`ko`)
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+
179
+ | Property | Value |
180
+ |----------------|--------------------------------------|
181
+ | Language | Korean (`ko`) |
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+ | Total Samples | 2,269,540 |
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+ | Train | 1,814,994 samples (78 files) |
184
+ | Test | 227,091 samples (78 files) |
185
+ | Validation | 227,455 samples (78 files) |
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+
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+ ### Russian (`ru`)
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+
189
+ | Property | Value |
190
+ |----------------|--------------------------------------|
191
+ | Language | Russian (`ru`) |
192
+ | Total Samples | 1,724,733 |
193
+ | Train | 1,380,404 samples (59 files) |
194
+ | Test | 171,678 samples (59 files) |
195
+ | Validation | 172,651 samples (59 files) |
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+
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+ ### Chinese Simplified (`zh_hans`)
198
+
199
+ | Property | Value |
200
+ |----------------|--------------------------------------|
201
+ | Language | Chinese (Simplified) (`zh_hans`) |
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+ | Total Samples | 2,335,343 |
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+ | Train | 1,914,948 samples (83 files) |
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+ | Test | 210,143 samples (73 files) |
205
+ | Validation | 210,252 samples (73 files) |
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+
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+ ### Chinese Traditional (`zh_hant`)
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+
209
+ | Property | Value |
210
+ |----------------|--------------------------------------|
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+ | Language | Chinese (Traditional) (`zh_hant`) |
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+ | Total Samples | 2,214,304 |
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+ | Train | 1,772,280 samples (77 files) |
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+ | Test | 221,867 samples (77 files) |
215
+ | Validation | 220,157 samples (77 files) |
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+
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+ ## Acknowledgements
218
+
219
+ The synthetic data generation pipeline is based on [SynthDoG](https://github.com/clovaai/donut/tree/master/synthdog) from the Donut project, with substantial modifications to support additional languages, custom rendering effects, structured bounding-box annotations (word/line/paragraph levels with reading-order graphs), and HDF5 output.
220
+
221
+ ## Citation
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+
223
+ If you use this dataset, please cite:
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+
225
+ ```bibtex
226
+ @misc{chesler2026ocr_synthetic_multilingual,
227
+ title = {{OCR-Synthetic-Multilingual-v1}},
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+ author = {Chesler, Ryan},
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+ year = {2026},
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+ publisher = {NVIDIA},
231
+ url = {https://huggingface.co/datasets/nvidia/OCR-Synthetic-Multilingual-v1},
232
+ note = {Synthetically generated multilingual OCR dataset built on a heavily modified SynthDoG pipeline}
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+ }
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+ ```
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