This is a slightly polished and packaged version of the Keras CRNN implementation and the published CRAFT text detection model. It provides a high level API for training a text detection and OCR pipeline.
Please see the documentation for more examples, including for training a custom model.
keras-ocr supports Python >= 3.6 and TensorFlow >= 2.0.0.
# To install from master pip install git+https://github.com/faustomorales/keras-ocr.git#egg=keras-ocr # To install from PyPi pip install keras-ocr
import matplotlib.pyplot as plt import keras_ocr # keras-ocr will automatically download pretrained # weights for the detector and recognizer. pipeline = keras_ocr.pipeline.Pipeline() # Get a set of three example images images = [ keras_ocr.tools.read(url) for url in [ 'https://upload.wikimedia.org/wikipedia/commons/b/bd/Army_Reserves_Recruitment_Banner_MOD_45156284.jpg', 'https://upload.wikimedia.org/wikipedia/commons/e/e8/FseeG2QeLXo.jpg', 'https://upload.wikimedia.org/wikipedia/commons/b/b4/EUBanana-500x112.jpg' ] ] # Each list of predictions in prediction_groups is a list of # (word, box) tuples. prediction_groups = pipeline.recognize(images) # Plot the predictions fig, axs = plt.subplots(nrows=len(images), figsize=(20, 20)) for ax, image, predictions in zip(axs, images, prediction_groups): keras_ocr.tools.drawAnnotations(image=image, predictions=predictions, ax=ax)
You may be wondering how the models in this package compare to existing cloud OCR APIs. We provide some metrics below and the notebook used to compute them using the first 1,000 images in the COCO-Text validation set. We limited it to 1,000 because the Google Cloud free tier is for 1,000 calls a month at the time of this writing. As always, caveats apply:
keras-ocrlatency values were computed using a Tesla P4 GPU on Google Colab.
scalerefers to the argument provided to
keras_ocr.pipelines.Pipeline()which determines the upscaling applied to the image prior to inference.
Why not compare to Tesseract? In every configuration I tried, Tesseract did very poorly on this test. Tesseract performs best on scans of books, not on incidental scene text like that in this dataset.
By default if a GPU is available Tensorflow tries to grab almost all of the available video memory, and this sucks if you're running multiple models with Tensorflow and Pytorch. Setting any value for the environment variable
MEMORY_GROWTH will force Tensorflow to dynamically allocate only as much GPU memory as is needed.
You can also specify a limit per Tensorflow process by setting the environment variable
MEMORY_ALLOCATED to any float, and this value is a float ratio of VRAM to the total amount present.
To apply these changes, call
keras_ocr.config.configure() at the top of your file where you import
To work on the project, start by doing the following. These instructions probably do not yet work for Windows but if a Windows user has some ideas for how to fix that it would be greatly appreciated (I don't have a Windows machine to test on at the moment).
# Install local dependencies for # code completion, etc. make init # Build the Docker container to run # tests and such. make build
make format-check type-check lint-check test.
To implement new features, please first file an issue proposing your change for discussion.
To report problems, please file an issue with sample code, expected results, actual results, and a complete traceback.
opencv-python-headlessbut I would prefer a different
opencvflavor. This is due to aleju/imgaug#473. You can uninstall the unwanted OpenCV flavor after installing
keras-ocr. We apologize for the inconvenience.