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import gradio as gr
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import sys
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sys.path.append('../src/')
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from utils.helpers import OCRD
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def run_ocrd_pipeline(img_path, status=gr.Progress(), binarize_mode='detailed', min_pixel_sum=30, median_bounds=(None, None), font_size=30):
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"""
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Executes the OCRD pipeline on an image from file loading to text overlay creation. This function orchestrates
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the calling of various OCRD class methods to process the image, extract and recognize text, and then overlay
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this text on the original image.
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Parameters:
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img_path (str): Path to the image file.
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binarize_mode (str): Mode to be used for image binarization. Can be 'detailed', 'fast', or 'no'.
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min_pixel_sum (int, optional): Minimum sum of pixels to consider a text line segmentation for extraction.
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If 'default', default values are applied.
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median_bounds (tuple, optional): Bounds to filter text line segmentations based on size relative to the median.
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If 'default', default values are applied.
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font_size (int, optional): Font size to be used in text overlay. If 'default', a default size or scaling logic is applied.
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Returns:
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Image: An image with overlay text, where text is extracted and recognized from the original image.
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This function handles:
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- Image binarization.
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- Text line segmentation.
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- Text line extraction and deskewing.
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- Optical character recognition on text lines.
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- Creating an image overlay with recognized text.
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"""
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efadt_kwargs = {}
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if min_pixel_sum != 'default':
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efadt_kwargs['min_pixel_sum'] = min_pixel_sum
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if median_bounds != 'default':
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efadt_kwargs['median_bounds'] = median_bounds
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ctoi_kwargs = {}
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if font_size != 'default':
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ctoi_kwargs['font_size'] = font_size
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ocrd = OCRD(img_path)
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status(0, desc='\nStep 1/5: Binarizing image...\n')
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binarized = ocrd.binarize_image(ocrd.image, binarize_mode)
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status(0, desc='\nStep 2/5: Segmenting textlines...\n')
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textline_segments = ocrd.segment_textlines(binarized)
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status(0, desc='\nStep 3/5: Extracting, filtering and de-skewing textlines...\n')
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image_scaled = ocrd.scale_image(ocrd.image)
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textline_images, _ = ocrd.extract_filter_and_deskew_textlines(image_scaled, textline_segments[...,0], **efadt_kwargs)
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status(0, desc='\nStep 4/5: OCR on textlines...\n')
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textline_preds = ocrd.ocr_on_textlines(textline_images)
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status(0, desc='\nStep 5/5: Creating output overlay image...')
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img_gen = ocrd.create_text_overlay_image(textline_images, textline_preds, (image_scaled.shape[0], image_scaled.shape[1]), **ctoi_kwargs)
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status(1, desc='\nJOB COMPLETED\n')
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return img_gen
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demo_data = [
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'../src/demo_data/act_image.jpg',
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'../src/demo_data/newjersey1_image.jpg',
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'../src/demo_data/newjersey2_image.jpg',
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'../src/demo_data/notes_image.jpg',
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'../src/demo_data/washington_image.jpg'
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]
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iface = gr.Interface(run_ocrd_pipeline,
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title="OCRD Pipeline",
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description="<ul><li>This interactive demo showcases an 'Optical Character Recognition Digitization' pipeline that processes \
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images to recognize text.</li> \
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<li>Steps include binarization, text line segmentation, extraction, filtering and deskewing as well as OCR. \
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Results are displayed as a generated overlay image.</li> \
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<li>Optimized for English; other languages (e.g. German) may require OCR model fine-tuning.</li> \
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<li>Uses free CPU-based compute, which is rather slow. A pipeline run will take up to 10 minutes. \
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For lengthy waits, pre-computed demo results are available for download: https://github.com/pluniak/ocrd/tree/main/src/demo_data.</li> \
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<li>Note: The demo is just a first version! OCR performance and computation speed can be optimized.</li> \
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<li>The demo is based on code from my GitHub repository: https://github.com/pluniak/ocrd/tree/main</li></ul>",
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inputs=[gr.Image(type='filepath', label='Input image')],
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outputs=gr.Image(label='Output image: overlay with recognized text', type='pil', format='jpeg'),
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examples=demo_data)
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iface.launch() |
