Optical Armenian Handwritten Character Recognition - Demo - © Calfa.fr¶
Manuscript in Erkat‘agir
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Expected results¶(in red boxes)
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page_gt.plot_image(with_char_boxes=True, figsize=(30,20), color='r', linewidth=2.5)
Automatic line detection¶
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detected_lines = strategy_gradient(page_gt)
page_empty = page_gt.clone()
page_empty.lines = {i:line for i,line in enumerate(detected_lines)}
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page_empty.plot_image(with_lines=True, figsize=(30,20), color='b', linewidth=2)
Load Convolutional Neural Network optimized for Line Transcription¶
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model = models.line_transcriptors
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len(page_empty.lines)
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line_id = 0
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page_empty.lines[line_id].get_image()
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page_gt.lines = {i:page_gt.lines[line_id] for i,line_id in enumerate(sorted(page_gt.lines, key=lambda line_id: page_gt.lines[line_id].get_coord()['y']))}
Expected results¶
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page_gt.lines[line_id].plot_image(with_char_boxes=True, figsize=(20,20), color='r', linewidth=3)
Single forward pass¶
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line_transcripted = model(page_empty.lines[line_id])
Automatic Detection & Recognition results¶
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line_transcripted.plot_image(with_char_boxes=True, figsize=(20,10), color='b', linewidth=3)
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print('ground truth:', page_gt.lines[line_id].get_class_names())
print(' prediction:', line_transcripted.get_class_names())