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feat(ocr): add self-hosted OCR engine support (Tesseract, EasyOCR) with multi-provider cross-checking

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Co-authored-by: christianlouis <361235+christianlouis@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot]
2026-02-23 20:59:43 +00:00
parent 9bba1f025f
commit 1f843fe460
7 changed files with 892 additions and 9 deletions
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app/tasks/process_document.py
+9 -8
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@@ -17,6 +17,7 @@ from app.tasks.extract_metadata_with_gpt import extract_metadata_with_gpt
from app.tasks.process_with_azure_document_intelligence import (
process_with_azure_document_intelligence,
)
from app.tasks.process_with_ocr import process_with_ocr
from app.tasks.retry_config import BaseTaskWithRetry
from app.utils import get_unique_filepath_with_counter, hash_file, log_task_progress
@@ -313,7 +314,7 @@ def process_document(
"Queued for forced OCR processing",
file_id=file_id,
)
process_with_azure_document_intelligence.delay(new_filename, file_id)
process_with_ocr.delay(new_filename, file_id)
return {"file": new_local_path, "status": "Queued for forced OCR", "file_id": file_id}
# If the file is not a PDF, skip embedded text check and convert to PDF first
@@ -411,12 +412,12 @@ def process_document(
file_id=file_id,
)
# Mark Azure OCR as skipped since we extracted text locally
# Mark OCR as skipped since we extracted text locally
log_task_progress(
task_id,
"process_with_azure_document_intelligence",
"skipped",
"Local text extraction succeeded, Azure OCR not needed",
"Local text extraction succeeded, OCR not needed",
file_id=file_id,
)
@@ -436,8 +437,8 @@ def process_document(
"file_id": file_id,
}
# 3. If no embedded text, queue Azure Document Intelligence processing
logger.info(f"[{task_id}] No embedded text found. Queueing Azure Document Intelligence processing")
# 3. If no embedded text, queue OCR processing
logger.info(f"[{task_id}] No embedded text found. Queueing OCR processing")
log_task_progress(
task_id,
"check_text",
@@ -446,12 +447,12 @@ def process_document(
file_id=file_id,
)
# Mark local text extraction as skipped since we're using Azure OCR
# Mark local text extraction as skipped since we're using cloud OCR
log_task_progress(
task_id,
"extract_text",
"skipped",
"No embedded text, using Azure OCR instead",
"No embedded text, using OCR instead",
file_id=file_id,
)
@@ -462,5 +463,5 @@ def process_document(
"Queued for OCR processing",
file_id=file_id,
)
process_with_azure_document_intelligence.delay(new_filename, file_id)
process_with_ocr.delay(new_filename, file_id)
return {"file": new_local_path, "status": "Queued for OCR", "file_id": file_id}