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For TrOCR, the cropped text lines will no longer be added to a list before prediction. Instead, for each batch size, the text line images will be collected and predictions will be made directly on them.

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vahidrezanezhad 2025-09-03 19:18:11 +02:00 • committed by kba
parent 0711166524
commit 542646791d
1 changed files with 1 additions and 2 deletions
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src/eynollah/utils/__init__.py
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@ -851,8 +851,7 @@ def putt_bb_of_drop_capitals_of_model_in_patches_in_layout(layout_in_patch, drop
all_drop_capital_pixels = np.sum(mask_of_drop_cpaital_in_early_layout==1) all_drop_capital_pixels = np.sum(mask_of_drop_cpaital_in_early_layout==1)
percent_text_to_all_in_drop = all_drop_capital_pixels_which_is_text_in_early_lo / float(all_drop_capital_pixels) percent_text_to_all_in_drop = all_drop_capital_pixels_which_is_text_in_early_lo / float(all_drop_capital_pixels)
if (areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) / float(w * h) > 0.6 and
if (areas_cnt_text[jj] * float(drop_only.shape[0] * drop_only.shape[1]) / float(w * h) > 0.7 and
percent_text_to_all_in_drop >= 0.3): percent_text_to_all_in_drop >= 0.3):
layout_in_patch[box0] = drop_capital_label layout_in_patch[box0] = drop_capital_label
else: else: