Extremely easy to use Text Detection module with CRAFT pre-trained model.
keras-craft aims to be production ready and supports features like batch inference (auto batching for images of different size) and tensorflow serving.
pip install git+https://github.com/notAI-tech/keras-craft
(the entire library)
docker run -p 8500:8500 bedapudi6788/keras-craft:generic-english
import craft_client
image_paths = [image_1, image_2, ..]
all_boxes = craft_client.detect(image_paths)
# Visualization
for image_path, boxes in zip(image_paths):
image_with_boxes_path = craft_client.draw_boxes_on_image(image_path, boxes)
print(image_with_boxes_path)
import keras_craft
detector = keras_craft.Detector()
image_paths = [image_1, image_2, ..]
all_boxes = detector.detect(image_paths)
# Visualization
for image_path, boxes in zip(image_paths):
image_with_boxes_path = keras_craft.draw_boxes_on_image(image_path, boxes)
print(image_with_boxes_path)
- Train different models for different use-cases. (various languages ..)
- Experiment with smaller model(s)
Credit for the core keras model, generic-english checkpoint .. goes to Fausto Morales and Clova.ai