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Running
on
T4
Running
on
T4
Update app.py
Browse files
app.py
CHANGED
@@ -83,11 +83,11 @@ def get_text_predictions(image, segment_predictions, recognizer):
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# Run demo code
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with gr.Blocks(theme=gr.themes.Monochrome(), title="Multicentury HTR Demo") as demo:
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gr.Markdown("# Multicentury HTR Demo")
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gr.Markdown("The HTR pipeline contains three components: text region detection, textline detection and handwritten text recognition
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The components run machine learning models that have been trained at the National Archives of Finland using mostly handwritten documents
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from 17th, 18th, 19th and 20th centuries. Best results are probably obtained when using high quality scans of documents with a regular layout
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Input image can be uploaded using the *Input image* window in the *Text content* tab, and the predicted text content will appear to the window
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on the right side of the image. Results of text region and text line detection can be viewed in the *Text regions* and *Text lines* tabs.)
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with gr.Tab("Text content"):
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with gr.Row():
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input_img = gr.Image(label="Input image", type="pil")
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# Run demo code
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with gr.Blocks(theme=gr.themes.Monochrome(), title="Multicentury HTR Demo") as demo:
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gr.Markdown("# Multicentury HTR Demo")
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gr.Markdown("""The HTR pipeline contains three components: text region detection, textline detection and handwritten text recognition.
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The components run machine learning models that have been trained at the National Archives of Finland using mostly handwritten documents
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from 17th, 18th, 19th and 20th centuries. Best results are probably obtained when using high quality scans of documents with a regular layout.
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Input image can be uploaded using the *Input image* window in the *Text content* tab, and the predicted text content will appear to the window
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on the right side of the image. Results of text region and text line detection can be viewed in the *Text regions* and *Text lines* tabs.""")
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with gr.Tab("Text content"):
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with gr.Row():
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input_img = gr.Image(label="Input image", type="pil")
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