Metadata-Version: 2.1
Name: onnxtr
Version: 0.5.1
Summary: Onnx Text Recognition (OnnxTR): docTR Onnx-Wrapper for high-performance OCR on documents.
Author-email: Felix Dittrich <felixdittrich92@gmail.com>
Maintainer: Felix Dittrich
License:                                  Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
        
Project-URL: repository, https://github.com/felixdittrich92/OnnxTR
Project-URL: tracker, https://github.com/felixdittrich92/OnnxTR/issues
Project-URL: changelog, https://github.com/felixdittrich92/OnnxTR/releases
Keywords: OCR,deep learning,computer vision,onnx,text detection,text recognition,docTR,document analysis,document processing
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: <4,>=3.9.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy<3.0.0,>=1.16.0
Requires-Dist: scipy<2.0.0,>=1.4.0
Requires-Dist: pypdfium2<5.0.0,>=4.11.0
Requires-Dist: pyclipper<2.0.0,>=1.2.0
Requires-Dist: shapely<3.0.0,>=1.6.0
Requires-Dist: rapidfuzz<4.0.0,>=3.0.0
Requires-Dist: langdetect<2.0.0,>=1.0.9
Requires-Dist: huggingface-hub<1.0.0,>=0.23.0
Requires-Dist: Pillow>=9.2.0
Requires-Dist: defusedxml>=0.7.0
Requires-Dist: anyascii>=0.3.2
Requires-Dist: tqdm>=4.30.0
Provides-Extra: cpu
Requires-Dist: onnxruntime>=1.11.0; extra == "cpu"
Requires-Dist: opencv-python<5.0.0,>=4.5.0; extra == "cpu"
Provides-Extra: gpu
Requires-Dist: onnxruntime-gpu>=1.11.0; extra == "gpu"
Requires-Dist: opencv-python<5.0.0,>=4.5.0; extra == "gpu"
Provides-Extra: cpu-headless
Requires-Dist: onnxruntime>=1.11.0; extra == "cpu-headless"
Requires-Dist: opencv-python-headless<5.0.0,>=4.5.0; extra == "cpu-headless"
Provides-Extra: gpu-headless
Requires-Dist: onnxruntime-gpu>=1.11.0; extra == "gpu-headless"
Requires-Dist: opencv-python-headless<5.0.0,>=4.5.0; extra == "gpu-headless"
Provides-Extra: html
Requires-Dist: weasyprint>=55.0; extra == "html"
Provides-Extra: viz
Requires-Dist: matplotlib>=3.1.0; extra == "viz"
Requires-Dist: mplcursors>=0.3; extra == "viz"
Provides-Extra: testing
Requires-Dist: pytest>=5.3.2; extra == "testing"
Requires-Dist: coverage[toml]>=4.5.4; extra == "testing"
Requires-Dist: requests>=2.20.0; extra == "testing"
Provides-Extra: quality
Requires-Dist: ruff>=0.1.5; extra == "quality"
Requires-Dist: mypy>=0.812; extra == "quality"
Requires-Dist: pre-commit>=2.17.0; extra == "quality"
Provides-Extra: dev
Requires-Dist: onnxruntime>=1.11.0; extra == "dev"
Requires-Dist: opencv-python<5.0.0,>=4.5.0; extra == "dev"
Requires-Dist: weasyprint>=55.0; extra == "dev"
Requires-Dist: matplotlib>=3.1.0; extra == "dev"
Requires-Dist: mplcursors>=0.3; extra == "dev"
Requires-Dist: pytest>=5.3.2; extra == "dev"
Requires-Dist: coverage[toml]>=4.5.4; extra == "dev"
Requires-Dist: requests>=2.20.0; extra == "dev"
Requires-Dist: ruff>=0.1.5; extra == "dev"
Requires-Dist: mypy>=0.812; extra == "dev"
Requires-Dist: pre-commit>=2.17.0; extra == "dev"

<p align="center">
  <img src="https://github.com/felixdittrich92/OnnxTR/raw/main/docs/images/logo.jpg" width="40%">
</p>

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
![Build Status](https://github.com/felixdittrich92/onnxtr/workflows/builds/badge.svg)
[![codecov](https://codecov.io/gh/felixdittrich92/OnnxTR/graph/badge.svg?token=WVFRCQBOLI)](https://codecov.io/gh/felixdittrich92/OnnxTR)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/4fff4d764bb14fb8b4f4afeb9587231b)](https://app.codacy.com/gh/felixdittrich92/OnnxTR/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[![CodeFactor](https://www.codefactor.io/repository/github/felixdittrich92/onnxtr/badge)](https://www.codefactor.io/repository/github/felixdittrich92/onnxtr)
[![Pypi](https://img.shields.io/badge/pypi-v0.5.0-blue.svg)](https://pypi.org/project/OnnxTR/)
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Felix92/OnnxTR-OCR)

> :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

![OCR_example](https://github.com/felixdittrich92/OnnxTR/raw/main/docs/images/ocr.png)

## 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]"
pip install "onnxtr[cpu-headless]"  # same as cpu but with opencv-headless
# with gpu support
pip install "onnxtr[gpu]"
pip install "onnxtr[gpu-headless]"  # same as gpu but with opencv-headless
# 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 `ocr_predictor` model for an example:

```python
from onnxtr.io import DocumentFile
from onnxtr.models import ocr_predictor, EngineConfig

model = ocr_predictor(
    det_arch='fast_base',  # detection architecture
    reco_arch='vitstr_base',  # recognition architecture
    det_bs=2, # detection batch size
    reco_bs=512, # 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)
    # Orientation specific parameters in combination with `assume_straight_pages=False` and/or `straighten_pages=True`
    disable_crop_orientation=False,  # set to `True` if the crop orientation classification should be disabled (default: False)
    disable_page_orientation=False,  # set to `True` if the general page orientation classification should be disabled (default: False)
    # DocumentBuilder specific parameters
    resolve_lines=True,  # whether words should be automatically grouped into lines (default: True)
    resolve_blocks=False,  # whether lines should be automatically grouped into blocks (default: False)
    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)
    # Advanced engine configuration options
    det_engine_cfg=EngineConfig(),  # detection model engine configuration (default: internal predefined configuration)
    reco_engine_cfg=EngineConfig(),  # recognition model engine configuration (default: internal predefined configuration)
    clf_engine_cfg=EngineConfig(),  # classification (orientation) model engine configuration (default: internal predefined configuration)
)
# 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()
```

![Visualization sample](https://github.com/felixdittrich92/OnnxTR/raw/main/docs/images/doctr_example_script.gif)

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()
```

![Synthesis sample](https://github.com/felixdittrich92/OnnxTR/raw/main/docs/images/synthesized_sample.png)

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]

```

<details>
  <summary>Advanced engine configuration options</summary>

You can also define advanced engine configurations for the models / predictors:

```python
from onnxruntime import SessionOptions

from onnxtr.models import ocr_predictor, EngineConfig

general_options = SessionOptions()  # For configuartion options see: https://onnxruntime.ai/docs/api/python/api_summary.html#sessionoptions
general_options.enable_cpu_mem_arena = False

# NOTE: The following would force to run only on the GPU if no GPU is available it will raise an error
# List of strings e.g. ["CUDAExecutionProvider", "CPUExecutionProvider"] or a list of tuples with the provider and its options e.g.
# [("CUDAExecutionProvider", {"device_id": 0}), ("CPUExecutionProvider", {"arena_extend_strategy": "kSameAsRequested"})]
providers = [("CUDAExecutionProvider", {"device_id": 0, "cudnn_conv_algo_search": "DEFAULT"})]  # For available providers see: https://onnxruntime.ai/docs/execution-providers/

engine_config = EngineConfig(
    session_options=general_options,
    providers=providers
)
# We use the default predictor with the custom engine configuration
# NOTE: You can define differnt engine configurations for detection, recognition and classification depending on your needs
predictor = ocr_predictor(
    det_engine_cfg=engine_config,
    reco_engine_cfg=engine_config,
    clf_engine_cfg=engine_config
)
```

</details>

## 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)
```

## Loading models from HuggingFace Hub

You can also load models from the HuggingFace Hub:

```python
from onnxtr.io import DocumentFile
from onnxtr.models import ocr_predictor, from_hub

img = DocumentFile.from_images(['<image_path>'])
# Load your model from the hub
model = from_hub('onnxtr/my-model')

# Pass it to the predictor
# If your model is a recognition model:
predictor = ocr_predictor(
    det_arch='db_mobilenet_v3_large',
    reco_arch=model
)

# If your model is a detection model:
predictor = ocr_predictor(
    det_arch=model,
    reco_arch='crnn_mobilenet_v3_small'
)

# Get your predictions
res = predictor(img)
```

HF Hub search: [here](https://huggingface.co/models?search=onnxtr).

Collection: [here](https://huggingface.co/collections/Felix92/onnxtr-66bf213a9f88f7346c90e842)

Or push your own models to the hub:

```python
from onnxtr.models import parseq, push_to_hf_hub, login_to_hub
from onnxtr.utils.vocabs import VOCABS

# Login to the hub
login_to_hub()

# Recogniton model
model = parseq("~/onnxtr-parseq-multilingual-v1.onnx", vocab=VOCABS["multilingual"])
push_to_hf_hub(
    model,
    model_name="onnxtr-parseq-multilingual-v1",
    task="recognition",  # The task for which the model is intended [detection, recognition, classification]
    arch="parseq",  # The name of the model architecture
    override=False  # Set to `True` if you want to override an existing model / repository
)

# Detection model
model = linknet_resnet18("~/onnxtr-linknet-resnet18.onnx")
push_to_hf_hub(
    model,
    model_name="onnxtr-linknet-resnet18",
    task="detection",
    arch="linknet_resnet18",
    override=True
)
```

## Models architectures

Credits where it's due: this repository provides ONNX models for the following architectures, converted from the docTR models:

### 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)** - v0.4.1        | ~0.57s / Page                 | **~0.25s / Page**             |
|**OnnxTR (CPU) 8-bit** - v0.4.1  | **~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                |
|PaddleOCR (CPU) - no cls - v2.7.3| ~1.27s / Page                 | ~0.38s / 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) - v0.4.1                | **~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                 |
|**PaddleOCR (GPU) - no cls - v2.7.3**| ~0.08s / Page                 | **~0.03s / 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}}
}
```

```bibtex
@misc{onnxtr2024,
    title={OnnxTR: Optical Character Recognition made seamless & accessible to anyone, powered by Onnx},
    author={Felix Dittrich},
    year={2024},
    publisher = {GitHub},
    howpublished = {\url{https://github.com/felixdittrich92/OnnxTR}}
}
```

## 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.
