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Add application file
Browse files- README.md +31 -7
- app.py +20 -0
- example_1.png +0 -0
- example_2.jpg +0 -0
- packages.txt +1 -0
- requirements.txt +3 -0
README.md
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title: CRAFT
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emoji:
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colorFrom:
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sdk: gradio
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sdk_version: 2.8.10
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app_file: app.py
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pinned: false
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license: mit
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---
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title: CRAFT OCR
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emoji: π
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colorFrom: pink
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colorTo: purple
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sdk: gradio
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app_file: app.py
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pinned: false
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---
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# Configuration
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`title`: _string_
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Display title for the Space
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`emoji`: _string_
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Space emoji (emoji-only character allowed)
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`colorFrom`: _string_
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Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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`colorTo`: _string_
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Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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`sdk`: _string_
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Can be either `gradio` or `streamlit`
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`sdk_version` : _string_
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Only applicable for `streamlit` SDK.
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See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
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`app_file`: _string_
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Path to your main application file (which contains either `gradio` or `streamlit` Python code).
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Path is relative to the root of the repository.
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`pinned`: _boolean_
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Whether the Space stays on top of your list.
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app.py
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import gradio as gr
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from craft_hw_ocr import OCR
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ocr = OCR.load_models()
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def do_ocr(inp):
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img, results = OCR.detection(inp, ocr[2])
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bboxes, text = OCR.recoginition(img, results, ocr[0], ocr[1])
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return OCR.visualize(img, results), text
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inputs = gr.inputs.Image()
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o1 = gr.outputs.Image()
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o2 = gr.outputs.Textbox()
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title = "CRAFT-OCR"
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description = "OCR of both handwriting and printed text using CRAFT Text detector and TrOCR recognition, detection of lines and extraction of them are happening here because TrOCR pre-trained models are modelled on IAM lines dataset and the same needs to be implemented here."
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examples=[['example_1.png'],['example_2.jpg']]
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article = "<p style='text-align: center'><a href='https://github.com/Vishnunkumar/craft_hw_ocr' target='_blank'>craft_hw_ocr</a></p><p style='text-align: center'> <p style='text-align: center'><a href='https://github.com/fcakyon/craft-text-detector' target='_blank'>craft-text-detector</a></p><p style='text-align: center'>"
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gr.Interface(fn=do_ocr, inputs=inputs, outputs=[o1, o2], title=title, description=description, article=article, examples=examples, enable_queue=True).launch()
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example_1.png
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example_2.jpg
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packages.txt
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python3-opencv
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requirements.txt
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craft-hw-ocr==1.1
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gradio
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opencv-python-headless==4.5.5.62
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