feat(ocr): fine-tune OCR quality criteria with stricter threshold and head-to-head comparison
- Raise quality acceptance threshold from 65→85 (configurable via TEXT_QUALITY_THRESHOLD) - Reject text with significant issues (excessive_typos, garbage_characters, incoherent_text, fragmented_sentences) even when score is above threshold (configurable via TEXT_QUALITY_SIGNIFICANT_ISSUES) - Add compare_text_quality() for AI-powered head-to-head comparison of original embedded text vs fresh OCR output - Update process_document to pass original text to OCR task for comparison - Update process_with_ocr to run comparison and keep the higher-quality text - Add new settings to settings_service.py metadata - Update docs/ConfigurationGuide.md with new settings - Add comprehensive tests for new threshold and comparison logic Co-authored-by: christianlouis <361235+christianlouis@users.noreply.github.com>
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copilot-swe-agent[bot]
@@ -8,7 +8,10 @@ task with a multi-engine OCR pipeline that:
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2. Merges/cross-checks the results using the configured AI model when more
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than one provider is active (see ``OCR_MERGE_STRATEGY``).
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3. Writes the best searchable PDF back to the working directory.
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4. Hands off to the page-rotation and metadata-extraction pipeline exactly as
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4. Optionally compares the OCR output against the original embedded text
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(passed as *original_text*) using a head-to-head AI review and keeps the
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higher-quality text for downstream processing.
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5. Hands off to the page-rotation and metadata-extraction pipeline exactly as
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the legacy Azure task did.
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"""
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@@ -22,20 +25,26 @@ from app.tasks.retry_config import BaseTaskWithRetry
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from app.tasks.rotate_pdf_pages import rotate_pdf_pages
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from app.utils import log_task_progress
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from app.utils.ocr_provider import OCRResult, embed_text_layer, get_ocr_providers, merge_ocr_results
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from app.utils.text_quality import compare_text_quality
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logger = logging.getLogger(__name__)
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@celery.task(base=BaseTaskWithRetry, bind=True)
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def process_with_ocr(self, filename: str, file_id: Optional[int] = None):
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def process_with_ocr(self, filename: str, file_id: Optional[int] = None, original_text: Optional[str] = None):
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"""Run the configured OCR providers on *filename* and continue the pipeline.
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When multiple OCR providers are configured the results are merged using the
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AI model (or a simpler strategy controlled by ``OCR_MERGE_STRATEGY``).
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If *original_text* is provided (the original embedded text that failed the
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quality check), the OCR result is compared against it using a head-to-head
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AI review. The higher-quality text is passed to downstream tasks.
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Args:
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filename: Base name of the file inside ``<workdir>/tmp/``.
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file_id: Optional database record ID passed through to downstream tasks.
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original_text: Optional original embedded text for head-to-head comparison.
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"""
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task_id = self.request.id
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log_task_progress(
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@@ -138,22 +147,107 @@ def process_with_ocr(self, filename: str, file_id: Optional[int] = None):
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file_id=file_id,
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)
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# ----------------------------------------------------------------
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# Head-to-head comparison with original embedded text (if provided)
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# ----------------------------------------------------------------
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final_text = extracted_text
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if original_text and original_text.strip() and extracted_text.strip():
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logger.info(f"[{task_id}] Original embedded text provided; running head-to-head quality comparison")
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"in_progress",
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"Comparing OCR result against original embedded text",
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file_id=file_id,
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)
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try:
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comparison = compare_text_quality(original_text, extracted_text)
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comparison_detail = (
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f"Original score: {comparison.original_score}/100, "
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f"OCR score: {comparison.ocr_score}/100, "
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f"Preferred: {comparison.preferred}\n"
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f"AI explanation: {comparison.explanation}"
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)
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logger.info(f"[{task_id}] OCR comparison – {comparison_detail}")
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if comparison.preferred == "original":
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# Original text is actually better – use it instead of OCR.
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final_text = original_text
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logger.info(
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f"[{task_id}] Original embedded text selected "
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f"(original={comparison.original_score} > ocr={comparison.ocr_score})"
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)
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"success",
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f"Original text preferred (original={comparison.original_score}/100 vs "
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f"ocr={comparison.ocr_score}/100)",
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file_id=file_id,
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detail=comparison_detail,
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)
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else:
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logger.info(
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f"[{task_id}] OCR text selected "
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f"(preferred={comparison.preferred!r}, "
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f"ocr={comparison.ocr_score}, original={comparison.original_score})"
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)
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"success",
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f"OCR text preferred (ocr={comparison.ocr_score}/100 vs "
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f"original={comparison.original_score}/100)",
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file_id=file_id,
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detail=comparison_detail,
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)
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except Exception as cmp_exc:
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logger.warning(f"[{task_id}] Head-to-head comparison failed ({cmp_exc}); keeping OCR text")
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"skipped",
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f"Comparison failed ({cmp_exc}); keeping OCR output",
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file_id=file_id,
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)
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elif original_text is not None:
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# original_text was provided but one side is empty – pick whichever has content.
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if not extracted_text.strip() and original_text.strip():
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final_text = original_text
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logger.info(f"[{task_id}] OCR returned empty text; falling back to original embedded text")
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"success",
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"OCR empty – using original embedded text",
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file_id=file_id,
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)
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else:
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log_task_progress(
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task_id,
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"compare_ocr_quality",
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"skipped",
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"No original text to compare; using OCR output",
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file_id=file_id,
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)
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log_task_progress(
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task_id,
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"process_with_ocr",
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"success",
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f"OCR complete for {filename}",
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file_id=file_id,
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detail=f"Extracted {len(extracted_text)} chars using {len(results)} provider(s)",
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detail=f"Extracted {len(extracted_text)} chars using {len(results)} provider(s); "
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f"final text length: {len(final_text)} chars",
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)
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# Continue pipeline: rotate pages (if needed), then extract metadata
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rotate_pdf_pages.delay(filename, extracted_text, rotation_data, file_id)
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rotate_pdf_pages.delay(filename, final_text, rotation_data, file_id)
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return {
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"file": filename,
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"searchable_pdf": searchable_pdf_path or tmp_file_path,
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"cleaned_text": extracted_text,
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"cleaned_text": final_text,
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"providers_used": [r.provider for r in results],
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}
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