902d423f3c
- Surya models lazy-load on first OCR request instead of at startup (saves ~3-4GB idle RAM — Kraken stays eager at ~16MB) - Process one page at a time in Surya engine (limits peak memory) - RECOGNITION_BATCH_SIZE=1, DETECTOR_BATCH_SIZE=1 (slower but fits in RAM) - Revert mem_limit back to 6GB (sufficient with these optimizations) - Render DPI stays at 200 Idle memory: ~2GB (Kraken only). Peak during OCR: ~5-6GB (Surya loaded). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
"""Surya OCR engine wrapper — transformer-based, handles typewritten and modern Latin handwriting."""
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import logging
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logger = logging.getLogger(__name__)
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_recognition_predictor = None
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_detection_predictor = None
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_loaded = False
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def load_models():
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"""Lazy-load Surya models on first use to save RAM at idle.
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Called automatically by extract_blocks(). Can also be called explicitly
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to pre-warm if desired.
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"""
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global _recognition_predictor, _detection_predictor, _loaded
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if _loaded:
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return
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logger.info("Loading Surya models (lazy, first OCR request)...")
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from surya.foundation import FoundationPredictor
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from surya.recognition import RecognitionPredictor
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from surya.detection import DetectionPredictor
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foundation_predictor = FoundationPredictor()
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_recognition_predictor = RecognitionPredictor(foundation_predictor)
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_detection_predictor = DetectionPredictor()
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_loaded = True
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logger.info("Surya models loaded successfully")
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def extract_blocks(images: list, language: str = "de") -> list[dict]:
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"""Run Surya OCR on a list of PIL images (one per page).
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Processes one page at a time to limit peak memory usage.
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Returns a flat list of block dicts with pageNumber, x, y, width, height,
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polygon, text, words. Coordinates are normalized to [0, 1].
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"""
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load_models()
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all_blocks = []
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for page_idx, image in enumerate(images):
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page_w, page_h = image.size
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# Process single page to limit peak memory
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predictions = _recognition_predictor([image], det_predictor=_detection_predictor)
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page_pred = predictions[0]
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for line in page_pred.text_lines:
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bbox = line.bbox
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x1, y1, x2, y2 = bbox
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polygon = None
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if hasattr(line, "polygon") and line.polygon and len(line.polygon) == 4:
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polygon = [
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[p[0] / page_w, p[1] / page_h]
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for p in line.polygon
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]
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words = []
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if hasattr(line, "words") and line.words:
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for word in line.words:
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words.append({
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"text": word.text,
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"confidence": word.confidence,
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})
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else:
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words = [{"text": line.text, "confidence": getattr(line, "confidence", 1.0)}]
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all_blocks.append({
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"pageNumber": page_idx,
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"x": x1 / page_w,
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"y": y1 / page_h,
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"width": (x2 - x1) / page_w,
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"height": (y2 - y1) / page_h,
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"polygon": polygon,
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"text": line.text,
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"words": words,
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})
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# Free page image after processing
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del image
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return all_blocks
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