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183_OCR_in_python_using_keras-ocr.py

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+# https://youtu.be/OPg_YuX6ThY
+
+"""keras-ocr.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1mBIzzB2Wa7hs8FOSeRmFyXBa7c1IEwuu
+
+Documentation:
+https://keras-ocr.readthedocs.io/en/latest/
+
+Based on: https://github.com/clovaai/CRAFT-pytorch
+"""
+
+# !pip install keras-ocr
+
+import keras_ocr
+from matplotlib import pyplot as plt
+
+# keras-ocr will automatically download pretrained
+# weights for the detector and recognizer.
+pipeline = keras_ocr.pipeline.Pipeline()
+
+# List of three example images
+images = [keras_ocr.tools.read(img) for img in ['/content/drive/My Drive/Colab Notebooks/data/billboard1.jpg',
+                                               '/content/drive/My Drive/Colab Notebooks/data/billboard2.jpg',
+                                                '/content/drive/My Drive/Colab Notebooks/data/handwritten.jpg'
+                                               ]
+]
+
+#Print shape...
+import numpy as np
+print(np.shape(images))
+
+# 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)