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Once the training process is complete we should save the trained model for further usage. To export the model we will use the exporter_main_v2.py script from Object Detection API. It prepares an object detection TensorFlow graph for inference using model configuration and a trained checkpoint. The script outputs associated checkpoint files, a SavedModel, and a copy of the model config:
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links-detector
📖 👆🏻 Links Detector makes printed links clickable via your smartphone camera. No need to type a link in, just scan and click on it.
The text of each link (right continuation of https:// bounding box) will be recognized by using Tesseract library. The recognition part will not be covered in this article, but you may find the complete code example of the application in links-detector repository.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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awesome-public-datasets repository
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The first and the most obvious approach would be to solve the Optical Character Recognition (OCR) task by recognizing the whole text of the image by using, let's say, Tesseract.js library. It returns the bounding boxes of the paragraphs, text lines, and text blocks along with the recognized text.
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The cool part about TensorBoard is that we may run it directly in Google Colab. However, if you're running the notebook in your local installation of Jupyter you may also install it as Python package and launch it from the terminal.
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labelImg
Discontinued LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
To do the labeling (to mark the locations of the objects that we're interested in, namely the https:// prefixes) we may use the LabelImg graphical image annotation tool.
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