https://github.com/notai-tech/keras-craft
Extremely easy to use Text Detection module with CRAFT pre-trained model.
https://github.com/notai-tech/keras-craft
craft cv east keras scene-text-detection scene-text-recognition tensorflow text-detection text-recognition
Last synced: 19 days ago
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Extremely easy to use Text Detection module with CRAFT pre-trained model.
- Host: GitHub
- URL: https://github.com/notai-tech/keras-craft
- Owner: notAI-tech
- License: mit
- Created: 2020-01-23T10:53:44.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T23:26:54.000Z (about 2 years ago)
- Last Synced: 2025-04-05T10:51:14.404Z (about 1 month ago)
- Topics: craft, cv, east, keras, scene-text-detection, scene-text-recognition, tensorflow, text-detection, text-recognition
- Language: Python
- Homepage:
- Size: 26.4 KB
- Stars: 8
- Watchers: 2
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# keras-craft
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.
# Installation
```pip install git+https://github.com/notAI-tech/keras-craft``` (the entire library)
# Usage (craft_client)
```bash
docker run -p 8500:8500 bedapudi6788/keras-craft:generic-english
```
```python
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)
```
# Usage (keras_craft)
```python
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)
```
# Example image_with_boxes
# To Do:
1. Train different models for different use-cases. (various languages ..)
2. Experiment with smaller model(s)
**Credit for the core keras model, generic-english checkpoint .. goes to [Fausto Morales](https://github.com/faustomorales/keras-ocr) and Clova.ai**