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chore: import upstream snapshot with attribution
2026-07-13 13:24:56 +08:00

45 lines
1.4 KiB
Python

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}")