| Name | onnxtr JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Onnx Text Recognition (OnnxTR): docTR Onnx-Wrapper for high-performance OCR on documents. |
| upload_time | 2024-05-13 10:41:00 |
| maintainer | Felix Dittrich |
| docs_url | None |
| author | None |
| requires_python | <4,>=3.9.0 |
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| keywords |
ocr
deep learning
computer vision
onnx
text detection
text recognition
doctr
document analysis
document processing
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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<p align="center">
<img src="https://github.com/felixdittrich92/OnnxTR/raw/main/docs/images/logo.jpg" width="40%">
</p>
[](LICENSE)
[](https://codecov.io/gh/felixdittrich92/OnnxTR)
[](https://app.codacy.com/gh/felixdittrich92/OnnxTR/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[](https://www.codefactor.io/repository/github/felixdittrich92/onnxtr)
[](https://pypi.org/project/OnnxTR/)
> :warning: Please note that this is a wrapper around the [doctr](https://github.com/mindee/doctr) library to provide a Onnx pipeline for docTR. For feature requests, which are not directly related to the Onnx pipeline, please refer to the base project.
**Optical Character Recognition made seamless & accessible to anyone, powered by Onnx**
What you can expect from this repository:
- efficient ways to parse textual information (localize and identify each word) from your documents
- a Onnx pipeline for docTR, a wrapper around the [doctr](https://github.com/mindee/doctr) library - no PyTorch or TensorFlow dependencies
- more lightweight package with faster inference latency and less required resources
- 8-Bit quantized models for faster inference on CPU
## Installation
### Prerequisites
Python 3.9 (or higher) and [pip](https://pip.pypa.io/en/stable/) are required to install OnnxTR.
### Latest release
You can then install the latest release of the package using [pypi](https://pypi.org/project/OnnxTR/) as follows:
**NOTE:**
For GPU support please take a look at: [ONNX Runtime](https://onnxruntime.ai/getting-started). Currently supported execution providers by default are: CPU, CUDA
- **Prerequisites:** CUDA & cuDNN needs to be installed before [Version table](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html).
```shell
pip install "onnxtr[cpu]"
# with gpu support
pip install "onnxtr[gpu]"
# with HTML support
pip install "onnxtr[html]"
# with support for visualization
pip install "onnxtr[viz]"
# with support for all dependencies
pip install "onnxtr[html, gpu, viz]"
```
### Reading files
Documents can be interpreted from PDF / Images / Webpages / Multiple page images using the following code snippet:
```python
from onnxtr.io import DocumentFile
# PDF
pdf_doc = DocumentFile.from_pdf("path/to/your/doc.pdf")
# Image
single_img_doc = DocumentFile.from_images("path/to/your/img.jpg")
# Webpage (requires `weasyprint` to be installed)
webpage_doc = DocumentFile.from_url("https://www.yoursite.com")
# Multiple page images
multi_img_doc = DocumentFile.from_images(["path/to/page1.jpg", "path/to/page2.jpg"])
```
### Putting it together
Let's use the default pretrained model for an example:
```python
from onnxtr.io import DocumentFile
from onnxtr.models import ocr_predictor
model = ocr_predictor(
det_arch='fast_base', # detection architecture
reco_arch='vitstr_base', # recognition architecture
det_bs=4, # detection batch size
reco_bs=1024, # recognition batch size
assume_straight_pages=True, # set to `False` if the pages are not straight (rotation, perspective, etc.) (default: True)
straighten_pages=False, # set to `True` if the pages should be straightened before final processing (default: False)
# Preprocessing related parameters
preserve_aspect_ratio=True, # set to `False` if the aspect ratio should not be preserved (default: True)
symmetric_pad=True, # set to `False` to disable symmetric padding (default: True)
# Additional parameters - meta information
detect_orientation=False, # set to `True` if the orientation of the pages should be detected (default: False)
detect_language=False, # set to `True` if the language of the pages should be detected (default: False)
# DocumentBuilder specific parameters
resolve_lines=True, # whether words should be automatically grouped into lines (default: True)
resolve_blocks=True, # whether lines should be automatically grouped into blocks (default: True)
paragraph_break=0.035, # relative length of the minimum space separating paragraphs (default: 0.035)
# OnnxTR specific parameters
# NOTE: 8-Bit quantized models are not available for FAST detection models and can in general lead to poorer accuracy
load_in_8_bit=False, # set to `True` to load 8-bit quantized models instead of the full precision onces (default: False)
)
# PDF
doc = DocumentFile.from_pdf("path/to/your/doc.pdf")
# Analyze
result = model(doc)
# Display the result (requires matplotlib & mplcursors to be installed)
result.show()
```
Or even rebuild the original document from its predictions:
```python
import matplotlib.pyplot as plt
synthetic_pages = result.synthesize()
plt.imshow(synthetic_pages[0]); plt.axis('off'); plt.show()
```
The `ocr_predictor` returns a `Document` object with a nested structure (with `Page`, `Block`, `Line`, `Word`, `Artefact`).
To get a better understanding of the document model, check out [documentation](https://mindee.github.io/doctr/modules/io.html#document-structure):
You can also export them as a nested dict, more appropriate for JSON format / render it or export as XML (hocr format):
```python
json_output = result.export() # nested dict
text_output = result.render() # human-readable text
xml_output = result.export_as_xml() # hocr format
for output in xml_output:
xml_bytes_string = output[0]
xml_element = output[1]
```
## Loading custom exported models
You can also load docTR custom exported models:
For exporting please take a look at the [doctr documentation](https://mindee.github.io/doctr/using_doctr/using_model_export.html#export-to-onnx).
```python
from onnxtr.models import ocr_predictor, linknet_resnet18, parseq
reco_model = parseq("path_to_custom_model.onnx", vocab="ABC")
det_model = linknet_resnet18("path_to_custom_model.onnx")
model = ocr_predictor(det_arch=det_model, reco_arch=reco_model)
```
## Models architectures
Credits where it's due: this repository is implementing, among others, architectures from published research papers.
### Text Detection
- DBNet: [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/pdf/1911.08947.pdf).
- LinkNet: [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://arxiv.org/pdf/1707.03718.pdf)
- FAST: [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://arxiv.org/pdf/2111.02394.pdf)
### Text Recognition
- CRNN: [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/pdf/1507.05717.pdf).
- SAR: [Show, Attend and Read:A Simple and Strong Baseline for Irregular Text Recognition](https://arxiv.org/pdf/1811.00751.pdf).
- MASTER: [MASTER: Multi-Aspect Non-local Network for Scene Text Recognition](https://arxiv.org/pdf/1910.02562.pdf).
- ViTSTR: [Vision Transformer for Fast and Efficient Scene Text Recognition](https://arxiv.org/pdf/2105.08582.pdf).
- PARSeq: [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://arxiv.org/pdf/2207.06966).
```python
predictor = ocr_predictor()
predictor.list_archs()
{
'detection archs':
[
'db_resnet34',
'db_resnet50',
'db_mobilenet_v3_large',
'linknet_resnet18',
'linknet_resnet34',
'linknet_resnet50',
'fast_tiny', # No 8-bit support
'fast_small', # No 8-bit support
'fast_base' # No 8-bit support
],
'recognition archs':
[
'crnn_vgg16_bn',
'crnn_mobilenet_v3_small',
'crnn_mobilenet_v3_large',
'sar_resnet31',
'master',
'vitstr_small',
'vitstr_base',
'parseq'
]
}
```
### Documentation
This repository is in sync with the [doctr](https://github.com/mindee/doctr) library, which provides a high-level API to perform OCR on documents.
This repository stays up-to-date with the latest features and improvements from the base project.
So we can refer to the [doctr documentation](https://mindee.github.io/doctr/) for more detailed information.
NOTE:
- `pretrained` is the default in OnnxTR, and not available as a parameter.
- docTR specific environment variables (e.g.: DOCTR_CACHE_DIR -> ONNXTR_CACHE_DIR) needs to be replaced with `ONNXTR_` prefix.
### Benchmarks
The CPU benchmarks was measured on a `i7-14700K Intel CPU`.
The GPU benchmarks was measured on a `RTX 4080 Nvidia GPU`.
Benchmarking performed on the FUNSD dataset and CORD dataset.
docTR / OnnxTR models used for the benchmarks are `fast_base` (full precision) | `db_resnet50` (8-bit variant) for detection and `crnn_vgg16_bn` for recognition.
The smallest combination in OnnxTR (docTR) of `db_mobilenet_v3_large` and `crnn_mobilenet_v3_small` takes as comparison `~0.17s / Page` on the FUNSD dataset and `~0.12s / Page` on the CORD dataset in **full precision**.
- CPU benchmarks:
|Library |FUNSD (199 pages) |CORD (900 pages) |
|--------------------------------|-------------------------------|-------------------------------|
|docTR (CPU) - v0.8.1 | ~1.29s / Page | ~0.60s / Page |
|**OnnxTR (CPU)** - v0.1.2 | ~0.57s / Page | **~0.25s / Page** |
|**OnnxTR (CPU) 8-bit** - v0.1.2 | **~0.38s / Page** | **~0.14s / Page** |
|EasyOCR (CPU) - v1.7.1 | ~1.96s / Page | ~1.75s / Page |
|**PyTesseract (CPU)** - v0.3.10 | **~0.50s / Page** | ~0.52s / Page |
|Surya (line) (CPU) - v0.4.4 | ~48.76s / Page | ~35.49s / Page |
- GPU benchmarks:
|Library |FUNSD (199 pages) |CORD (900 pages) |
|--------------------------------|-------------------------------|-------------------------------|
|docTR (GPU) - v0.8.1 | ~0.07s / Page | ~0.05s / Page |
|**docTR (GPU) float16** - v0.8.1| **~0.06s / Page** | **~0.03s / Page** |
|OnnxTR (GPU) - v0.1.2 | **~0.06s / Page** | ~0.04s / Page |
|EasyOCR (GPU) - v1.7.1 | ~0.31s / Page | ~0.19s / Page |
|Surya (GPU) float16 - v0.4.4 | ~3.70s / Page | ~2.81s / Page |
## Citation
If you wish to cite please refer to the base project citation, feel free to use this [BibTeX](http://www.bibtex.org/) reference:
```bibtex
@misc{doctr2021,
title={docTR: Document Text Recognition},
author={Mindee},
year={2021},
publisher = {GitHub},
howpublished = {\url{https://github.com/mindee/doctr}}
}
```
## License
Distributed under the Apache 2.0 License. See [`LICENSE`](https://github.com/felixdittrich92/OnnxTR?tab=Apache-2.0-1-ov-file#readme) for more information.
Raw data
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"maintainer": "Felix Dittrich",
"docs_url": null,
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"maintainer_email": null,
"keywords": "OCR, deep learning, computer vision, onnx, text detection, text recognition, docTR, document analysis, document processing",
"author": null,
"author_email": "Felix Dittrich <felixdittrich92@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/f2/e3/46beb7206a64157124108705d0651a14033875a68a73ac346b87d0b85c16/onnxtr-0.2.0.tar.gz",
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"description": "<p align=\"center\">\n <img src=\"https://github.com/felixdittrich92/OnnxTR/raw/main/docs/images/logo.jpg\" width=\"40%\">\n</p>\n\n[](LICENSE)\n\n[](https://codecov.io/gh/felixdittrich92/OnnxTR)\n[](https://app.codacy.com/gh/felixdittrich92/OnnxTR/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)\n[](https://www.codefactor.io/repository/github/felixdittrich92/onnxtr)\n[](https://pypi.org/project/OnnxTR/)\n\n> :warning: Please note that this is a wrapper around the [doctr](https://github.com/mindee/doctr) library to provide a Onnx pipeline for docTR. For feature requests, which are not directly related to the Onnx pipeline, please refer to the base project.\n\n**Optical Character Recognition made seamless & accessible to anyone, powered by Onnx**\n\nWhat you can expect from this repository:\n\n- efficient ways to parse textual information (localize and identify each word) from your documents\n- a Onnx pipeline for docTR, a wrapper around the [doctr](https://github.com/mindee/doctr) library - no PyTorch or TensorFlow dependencies\n- more lightweight package with faster inference latency and less required resources\n- 8-Bit quantized models for faster inference on CPU\n\n\n\n## Installation\n\n### Prerequisites\n\nPython 3.9 (or higher) and [pip](https://pip.pypa.io/en/stable/) are required to install OnnxTR.\n\n### Latest release\n\nYou can then install the latest release of the package using [pypi](https://pypi.org/project/OnnxTR/) as follows:\n\n**NOTE:**\n\nFor GPU support please take a look at: [ONNX Runtime](https://onnxruntime.ai/getting-started). Currently supported execution providers by default are: CPU, CUDA\n\n- **Prerequisites:** CUDA & cuDNN needs to be installed before [Version table](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html).\n\n```shell\npip install \"onnxtr[cpu]\"\n# with gpu support\npip install \"onnxtr[gpu]\"\n# with HTML support\npip install \"onnxtr[html]\"\n# with support for visualization\npip install \"onnxtr[viz]\"\n# with support for all dependencies\npip install \"onnxtr[html, gpu, viz]\"\n```\n\n### Reading files\n\nDocuments can be interpreted from PDF / Images / Webpages / Multiple page images using the following code snippet:\n\n```python\nfrom onnxtr.io import DocumentFile\n# PDF\npdf_doc = DocumentFile.from_pdf(\"path/to/your/doc.pdf\")\n# Image\nsingle_img_doc = DocumentFile.from_images(\"path/to/your/img.jpg\")\n# Webpage (requires `weasyprint` to be installed)\nwebpage_doc = DocumentFile.from_url(\"https://www.yoursite.com\")\n# Multiple page images\nmulti_img_doc = DocumentFile.from_images([\"path/to/page1.jpg\", \"path/to/page2.jpg\"])\n```\n\n### Putting it together\n\nLet's use the default pretrained model for an example:\n\n```python\nfrom onnxtr.io import DocumentFile\nfrom onnxtr.models import ocr_predictor\n\nmodel = ocr_predictor(\n det_arch='fast_base', # detection architecture\n reco_arch='vitstr_base', # recognition architecture\n det_bs=4, # detection batch size\n reco_bs=1024, # recognition batch size\n assume_straight_pages=True, # set to `False` if the pages are not straight (rotation, perspective, etc.) (default: True)\n straighten_pages=False, # set to `True` if the pages should be straightened before final processing (default: False)\n # Preprocessing related parameters\n preserve_aspect_ratio=True, # set to `False` if the aspect ratio should not be preserved (default: True)\n symmetric_pad=True, # set to `False` to disable symmetric padding (default: True)\n # Additional parameters - meta information\n detect_orientation=False, # set to `True` if the orientation of the pages should be detected (default: False)\n detect_language=False, # set to `True` if the language of the pages should be detected (default: False)\n # DocumentBuilder specific parameters\n resolve_lines=True, # whether words should be automatically grouped into lines (default: True)\n resolve_blocks=True, # whether lines should be automatically grouped into blocks (default: True)\n paragraph_break=0.035, # relative length of the minimum space separating paragraphs (default: 0.035)\n # OnnxTR specific parameters\n # NOTE: 8-Bit quantized models are not available for FAST detection models and can in general lead to poorer accuracy\n load_in_8_bit=False, # set to `True` to load 8-bit quantized models instead of the full precision onces (default: False)\n)\n# PDF\ndoc = DocumentFile.from_pdf(\"path/to/your/doc.pdf\")\n# Analyze\nresult = model(doc)\n# Display the result (requires matplotlib & mplcursors to be installed)\nresult.show()\n```\n\n\n\nOr even rebuild the original document from its predictions:\n\n```python\nimport matplotlib.pyplot as plt\n\nsynthetic_pages = result.synthesize()\nplt.imshow(synthetic_pages[0]); plt.axis('off'); plt.show()\n```\n\n\n\nThe `ocr_predictor` returns a `Document` object with a nested structure (with `Page`, `Block`, `Line`, `Word`, `Artefact`).\nTo get a better understanding of the document model, check out [documentation](https://mindee.github.io/doctr/modules/io.html#document-structure):\n\nYou can also export them as a nested dict, more appropriate for JSON format / render it or export as XML (hocr format):\n\n```python\njson_output = result.export() # nested dict\ntext_output = result.render() # human-readable text\nxml_output = result.export_as_xml() # hocr format\nfor output in xml_output:\n xml_bytes_string = output[0]\n xml_element = output[1]\n\n```\n\n## Loading custom exported models\n\nYou can also load docTR custom exported models:\nFor exporting please take a look at the [doctr documentation](https://mindee.github.io/doctr/using_doctr/using_model_export.html#export-to-onnx).\n\n```python\nfrom onnxtr.models import ocr_predictor, linknet_resnet18, parseq\n\nreco_model = parseq(\"path_to_custom_model.onnx\", vocab=\"ABC\")\ndet_model = linknet_resnet18(\"path_to_custom_model.onnx\")\nmodel = ocr_predictor(det_arch=det_model, reco_arch=reco_model)\n```\n\n## Models architectures\n\nCredits where it's due: this repository is implementing, among others, architectures from published research papers.\n\n### Text Detection\n\n- DBNet: [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/pdf/1911.08947.pdf).\n- LinkNet: [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://arxiv.org/pdf/1707.03718.pdf)\n- FAST: [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://arxiv.org/pdf/2111.02394.pdf)\n\n### Text Recognition\n\n- CRNN: [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/pdf/1507.05717.pdf).\n- SAR: [Show, Attend and Read:A Simple and Strong Baseline for Irregular Text Recognition](https://arxiv.org/pdf/1811.00751.pdf).\n- MASTER: [MASTER: Multi-Aspect Non-local Network for Scene Text Recognition](https://arxiv.org/pdf/1910.02562.pdf).\n- ViTSTR: [Vision Transformer for Fast and Efficient Scene Text Recognition](https://arxiv.org/pdf/2105.08582.pdf).\n- PARSeq: [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://arxiv.org/pdf/2207.06966).\n\n```python\npredictor = ocr_predictor()\npredictor.list_archs()\n{\n 'detection archs':\n [\n 'db_resnet34',\n 'db_resnet50',\n 'db_mobilenet_v3_large',\n 'linknet_resnet18',\n 'linknet_resnet34',\n 'linknet_resnet50',\n 'fast_tiny', # No 8-bit support\n 'fast_small', # No 8-bit support\n 'fast_base' # No 8-bit support\n ],\n 'recognition archs':\n [\n 'crnn_vgg16_bn',\n 'crnn_mobilenet_v3_small',\n 'crnn_mobilenet_v3_large',\n 'sar_resnet31',\n 'master',\n 'vitstr_small',\n 'vitstr_base',\n 'parseq'\n ]\n}\n```\n\n### Documentation\n\nThis repository is in sync with the [doctr](https://github.com/mindee/doctr) library, which provides a high-level API to perform OCR on documents.\nThis repository stays up-to-date with the latest features and improvements from the base project.\nSo we can refer to the [doctr documentation](https://mindee.github.io/doctr/) for more detailed information.\n\nNOTE:\n\n- `pretrained` is the default in OnnxTR, and not available as a parameter.\n- docTR specific environment variables (e.g.: DOCTR_CACHE_DIR -> ONNXTR_CACHE_DIR) needs to be replaced with `ONNXTR_` prefix.\n\n### Benchmarks\n\nThe CPU benchmarks was measured on a `i7-14700K Intel CPU`.\n\nThe GPU benchmarks was measured on a `RTX 4080 Nvidia GPU`.\n\nBenchmarking performed on the FUNSD dataset and CORD dataset.\n\ndocTR / OnnxTR models used for the benchmarks are `fast_base` (full precision) | `db_resnet50` (8-bit variant) for detection and `crnn_vgg16_bn` for recognition.\n\nThe smallest combination in OnnxTR (docTR) of `db_mobilenet_v3_large` and `crnn_mobilenet_v3_small` takes as comparison `~0.17s / Page` on the FUNSD dataset and `~0.12s / Page` on the CORD dataset in **full precision**.\n\n- CPU benchmarks:\n\n|Library |FUNSD (199 pages) |CORD (900 pages) |\n|--------------------------------|-------------------------------|-------------------------------|\n|docTR (CPU) - v0.8.1 | ~1.29s / Page | ~0.60s / Page |\n|**OnnxTR (CPU)** - v0.1.2 | ~0.57s / Page | **~0.25s / Page** |\n|**OnnxTR (CPU) 8-bit** - v0.1.2 | **~0.38s / Page** | **~0.14s / Page** |\n|EasyOCR (CPU) - v1.7.1 | ~1.96s / Page | ~1.75s / Page |\n|**PyTesseract (CPU)** - v0.3.10 | **~0.50s / Page** | ~0.52s / Page |\n|Surya (line) (CPU) - v0.4.4 | ~48.76s / Page | ~35.49s / Page |\n\n- GPU benchmarks:\n\n|Library |FUNSD (199 pages) |CORD (900 pages) |\n|--------------------------------|-------------------------------|-------------------------------|\n|docTR (GPU) - v0.8.1 | ~0.07s / Page | ~0.05s / Page |\n|**docTR (GPU) float16** - v0.8.1| **~0.06s / Page** | **~0.03s / Page** |\n|OnnxTR (GPU) - v0.1.2 | **~0.06s / Page** | ~0.04s / Page |\n|EasyOCR (GPU) - v1.7.1 | ~0.31s / Page | ~0.19s / Page |\n|Surya (GPU) float16 - v0.4.4 | ~3.70s / Page | ~2.81s / Page |\n\n## Citation\n\nIf you wish to cite please refer to the base project citation, feel free to use this [BibTeX](http://www.bibtex.org/) reference:\n\n```bibtex\n@misc{doctr2021,\n title={docTR: Document Text Recognition},\n author={Mindee},\n year={2021},\n publisher = {GitHub},\n howpublished = {\\url{https://github.com/mindee/doctr}}\n}\n```\n\n## License\n\nDistributed under the Apache 2.0 License. See [`LICENSE`](https://github.com/felixdittrich92/OnnxTR?tab=Apache-2.0-1-ov-file#readme) for more information.\n",
"bugtrack_url": null,
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