Synced repo using 'sync_with_huggingface' Github Action

gradio_app.py

@@ -22,7 +22,6 @@ from surya.model.recognition.model import load_model as load_rec_model
 from surya.model.recognition.processor import load_processor as load_rec_processor
 from surya.model.table_rec.model import load_model as load_table_model
 from surya.model.table_rec.processor import load_processor as load_table_processor
-from surya.model.ocr_error.model import load_model as load_ocr_error_model, load_tokenizer as load_ocr_error_processor
 from surya.postprocessing.heatmap import draw_polys_on_image, draw_bboxes_on_image
 from surya.ocr import run_ocr
 from surya.postprocessing.text import draw_text_on_image
@@ -32,9 +31,7 @@ from surya.input.langs import replace_lang_with_code
 from surya.schema import OCRResult, TextDetectionResult, LayoutResult, TableResult
 from surya.settings import settings
 from surya.tables import batch_table_recognition
-from surya.postprocessing.util import rescale_bbox
-from pdftext.extraction import plain_text_output
-from surya.ocr_error import batch_ocr_error_detection
+from surya.postprocessing.util import rescale_bboxes, rescale_bbox
 
 
 def load_det_cached():
@@ -49,34 +46,6 @@ def load_layout_cached():
 def load_table_cached():
     return load_table_model(), load_table_processor()
 
-def load_ocr_error_cached():
-    return load_ocr_error_model(), load_ocr_error_processor()
-
-
-def run_ocr_errors(pdf_file, page_count, sample_len=512, max_samples=10, max_pages=15):
-    # Sample the text from the middle of the PDF
-    page_middle = page_count // 2
-    page_range = range(max(page_middle - max_pages, 0), min(page_middle + max_pages, page_count))
-    text = plain_text_output(pdf_file, page_range=page_range)
-
-    sample_gap = len(text) // max_samples
-    if len(text) == 0 or sample_gap == 0:
-        return "This PDF has no text or very little text", ["no text"]
-
-    if sample_gap < sample_len:
-        sample_gap = sample_len
-
-    # Split the text into samples for the model
-    samples = []
-    for i in range(0, len(text), sample_gap):
-        samples.append(text[i:i + sample_len])
-
-    results = batch_ocr_error_detection(samples, ocr_error_model, ocr_error_processor)
-    label = "This PDF has good text."
-    if results.labels.count("bad") / len(results.labels) > .2:
-        label = "This PDF may have garbled or bad OCR text."
-    return label, results.labels
-
 
 def text_detection(img) -> (Image.Image, TextDetectionResult):
     pred = batch_text_detection([img], det_model, det_processor)[0]
@@ -179,7 +148,6 @@ det_model, det_processor = load_det_cached()
 rec_model, rec_processor = load_rec_cached()
 layout_model, layout_processor = load_layout_cached()
 table_model, table_processor = load_table_cached()
-ocr_error_model, ocr_error_processor = load_ocr_error_cached()
 
 with gr.Blocks(title="Surya") as demo:
     gr.Markdown("""
@@ -211,8 +179,6 @@ with gr.Blocks(title="Surya") as demo:
             use_pdf_boxes_ckb = gr.Checkbox(label="Use PDF table boxes", value=True, info="Table recognition only: Use the bounding boxes from the PDF file vs text detection model.")
             skip_table_detection_ckb = gr.Checkbox(label="Skip table detection", value=False, info="Table recognition only: Skip table detection and treat the whole image/page as a table.")
             table_rec_btn = gr.Button("Run Table Rec")
-
-            ocr_errors_btn = gr.Button("Run bad PDF text detection")
         with gr.Column():
             result_img = gr.Image(label="Result image")
             result_json = gr.JSON(label="Result json")
@@ -284,18 +250,5 @@ with gr.Blocks(title="Surya") as demo:
             inputs=[in_img, in_file, in_num, use_pdf_boxes_ckb, skip_table_detection_ckb],
             outputs=[result_img, result_json]
         )
-        # Run bad PDF text detection
-        def ocr_errors_pdf(file, page_count, sample_len=512, max_samples=10, max_pages=15):
-            if file.endswith('.pdf'):
-                count = count_pdf(file)
-            else:
-                raise gr.Error("This feature only works with PDFs.", duration=5)
-            label, results = run_ocr_errors(file, count)
-            return gr.update(label="Result json:" + label, value=results)
-        ocr_errors_btn.click(
-            fn=ocr_errors_pdf,
-            inputs=[in_file, in_num, use_pdf_boxes_ckb, skip_table_detection_ckb],
-            outputs=[result_json]
-        )
 
 demo.launch()

requirements.txt

@@ -1,4 +1,4 @@
 torch==2.5.1
-surya-ocr==0.8.1
+surya-ocr==0.7.0
 gradio==5.8.0
 huggingface-hub==0.26.3