Added blog post "docTR joins PyTorch Ecosystem: From Pixels to Data, Building a Recognition Pipeline with PyTorch and docTR"

Signed-off-by: Chris Abraham <cjyabraham@gmail.com>

Author: cjyabraham

_posts/2024-12-18-doctr-joins-pytorch-ecosystem.md

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+---
+layout: blog_detail
+title: "docTR joins PyTorch Ecosystem: From Pixels to Data, Building a Recognition Pipeline with PyTorch and docTR"
+author: Olivier Dulcy & Sebastian Olivera, Mindee
+---
+
+![docTR logo](/assets/images/doctr-joins-pytorch-ecosystem/fg1.png){:style="width:100%;display: block;max-width:400px; margin-left:auto; margin-right:auto;"}
+
+We’re thrilled to announce that the docTR project has been integrated into the PyTorch ecosystem! This integration ensures that docTR aligns with PyTorch’s standards and practices, giving developers a reliable, community-backed solution for powerful OCR workflows.
+
+**For more information on what it means to be a PyTorch ecosystem project, see the [PyTorch Ecosystem Tools page](https://pytorch.org/ecosystem/).**
+
+
+## About docTR
+
+docTR is an Apache 2.0 project developed and distributed by [Mindee](https://www.mindee.com/) to help developers integrate OCR capabilities into applications with no prior knowledge required.
+
+To quickly and efficiently extract text information, docTR uses a two-stage approach:
+
+
+
+* First, it performs text **detection** to localize words.
+* Then, it conducts text **recognition** to identify all characters in a word.
+
+**Detection** and **recognition** are performed by state-of-the-art models written in PyTorch. To learn more about this approach, you can refer [to the docTR documentation](https://mindee.github.io/doctr/using_doctr/using_models.html).
+
+docTR enhances the user experience in PyTorch projects by providing high-performance OCR capabilities right out of the box. Its specially designed models require minimal to no fine-tuning for common use cases, allowing developers to quickly integrate advanced document analysis features.
+
+
+## Local installation
+
+docTR requires Python >= 3.10 and supports Windows, Mac and Linux. Please refer to our [README](https://github.com/mindee/doctr?tab=readme-ov-file#installation) for necessary dependencies for MacBook with the M1 chip.
+
+```
+pip3 install -U pip
+pip3 install "python-doctr[torch,viz]"
+```
+
+This will install docTR along with the latest version of PyTorch.
+
+
+```
+Note: docTR also provides docker images for an easy deployment, such as a part of Kubernetes cluster.
+```
+
+
+
+## Text recognition
+
+Now, let’s try docTR’s OCR recognition on this sample:
+
+
+![OCR sample](/assets/images/doctr-joins-pytorch-ecosystem/fg2.png){:style="width:100%;display: block;max-width:300px; margin-left:auto; margin-right:auto;"}
+
+
+The OCR recognition model expects an image with only one word on it and will output the predicted word with a confidence score. You can use the following snippet to test OCR capabilities from docTR:
+
+```
+python
+from doctr.io import DocumentFile
+from doctr.models import recognition_predictor
+
+doc = DocumentFile.from_images("/path/to/image")
+
+# Load the OCR model
+# This will download pre-trained models hosted by Mindee
+model = recognition_predictor(pretrained=True)
+
+result = model(doc)
+print(result)
+```
+
+Here, the most important line of code is `model = recognition_predictor(pretrained=True)`. This will load a default text recognition model,** **`crnn_vgg16_bn`, but you can select other models through the `arch` parameter. You can check out the [available architectures](https://mindee.github.io/doctr/using_doctr/using_models.html).
+
+When run on the sample, the recognition predictor retrieves the following data: `[('MAGAZINE', 0.9872216582298279)]`
+
+
+```
+Note: using the DocumentFile object docTR provides an easy way to manipulate PDF or Images.
+```
+
+
+
+## Text detection
+
+The last example was a crop on a single word. Now, what about an image with several words on it, like this one?
+
+
+![photo of magazines](/assets/images/doctr-joins-pytorch-ecosystem/fg3.jpg){:style="width:100%;display: block;max-width:200px; margin-left:auto; margin-right:auto;"}
+
+
+A text detection model is used before the text recognition to output a segmentation map representing the location of the text. Following that, the text recognition is applied on every detected patch.
+
+Below is a snippet to run only the detection part:
+
+```
+from doctr.io import DocumentFile
+from doctr.models import detection_predictor
+from matplotlib import pyplot as plt
+from doctr.utils.geometry import detach_scores
+from doctr.utils.visualization import draw_boxes
+
+doc = DocumentFile.from_images("path/to/my/file")
+model = detection_predictor(pretrained=True)
+
+result = model(doc)
+
+draw_boxes(detach_scores([result[0]["words"]])[0][0], doc[0])
+plt.axis('off')
+plt.show()
+```
+
+Running it on the full sample yields the following:
+
+
+![photo of magazines](/assets/images/doctr-joins-pytorch-ecosystem/fg4.png){:style="width:100%;display: block;max-width:200px; margin-left:auto; margin-right:auto;"}
+
+
+Similarly to the text recognition, `detection_predictor` will load a default model (`fast_base `here). You can also load another one by providing it through the `arch` parameter.
+
+
+## The full implementation
+
+Now, let’s plug both components into the same pipeline. 
+
+Conveniently, docTR provides a wrapper that does exactly that for us:
+
+```
+from doctr.io import DocumentFile
+from doctr.models import ocr_predictor
+
+doc = DocumentFile.from_images("/path/to/image")
+
+model = ocr_predictor(pretrained=True, assume_straight_pages=False)
+
+result = model(doc)
+result.show()
+```
+
+![photo of magazines](/assets/images/doctr-joins-pytorch-ecosystem/fg5.png){:style="width:100%;display: block;max-width:200px; margin-left:auto; margin-right:auto;"}
+
+The last line should display a matplotlib window which shows the detected patches. Hovering the mouse over them will display their contents.
+
+You can also do more with this output, such as reconstituting a synthetic document like so:
+
+```
+import matplotlib.pyplot as plt
+
+synthetic_pages = result.synthesize()
+plt.imshow(synthetic_pages[0])
+plt.axis('off')
+plt.show()
+```
+
+![black text on white](/assets/images/doctr-joins-pytorch-ecosystem/fg6.png){:style="width:100%;display: block;max-width:200px; margin-left:auto; margin-right:auto;"}
+
+
+The pipeline is highly customizable, where you can modify the detection or recognition model behaviors by passing arguments to the `ocr_predictor`. Please refer to the [documentation](https://mindee.github.io/doctr/using_doctr/using_models.html) to learn more about it. 
+
+
+## Conclusion
+
+We’re excited to welcome docTR into the PyTorch Ecosystem, where it seamlessly integrates with PyTorch pipelines to deliver state-of-the-art OCR capabilities right out of the box. 
+
+By empowering developers to quickly extract text from images or PDFs using familiar tooling, docTR simplifies complex document analysis tasks and enhances the overall PyTorch experience.
+
+We invite you to explore the [docTR GitHub repository](https://github.com/mindee/doctr), join the [docTR community on Slack](https://slack.mindee.com/), and reach out at contact@mindee.com for inquiries or collaboration opportunities. 
+
+Together, we can continue to push the boundaries of document understanding and develop even more powerful, accessible tools for everyone in the PyTorch community.