Suya OCR API — vLLM-backed, OpenAI-compatible OCR service

FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM:
legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint,
a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual
example outputs, and quantization/concurrency benchmarks.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

surya/scripts/ocr_text.py

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+import os
+import click
+import json
+import time
+from collections import defaultdict
+
+from surya.inference import SuryaInferenceManager
+from surya.logging import configure_logging, get_logger
+from surya.recognition import RecognitionPredictor
+from surya.scripts.config import CLILoader
+
+configure_logging()
+logger = get_logger()
+
+
+@click.command(help="OCR text — full-page OCR (one VLM call per page).")
+@CLILoader.common_options
+def ocr_text_cli(input_path: str, **kwargs):
+    # Full-page OCR is the default path: one VLM call per page returns layout
+    # + content together. Pages whose full-page output fails to parse fall
+    # back to layout + per-block OCR automatically (see RecognitionPredictor).
+    loader = CLILoader(input_path, kwargs, highres=True)
+
+    manager = SuryaInferenceManager()
+    rec_predictor = RecognitionPredictor(manager)
+
+    start = time.time()
+    page_results = rec_predictor(loader.highres_images, full_page=True)
+
+    if loader.debug:
+        logger.debug(f"OCR took {time.time() - start:.2f} seconds")
+
+    out_preds = defaultdict(list)
+    for name, page in zip(loader.names, page_results):
+        out_pred = page.model_dump()
+        out_pred["page"] = len(out_preds[name]) + 1
+        out_preds[name].append(out_pred)
+
+    with open(
+        os.path.join(loader.result_path, "results.json"), "w+", encoding="utf-8"
+    ) as f:
+        json.dump(out_preds, f, ensure_ascii=False)
+
+    logger.info(f"Wrote results to {loader.result_path}")
+
+
+if __name__ == "__main__":
+    ocr_text_cli()