File size: 2,512 Bytes
2af6ae7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | #!/usr/bin/env python3
"""Portable logical-order Persian line recognition for the Bina 0.2 family."""
from __future__ import annotations
import argparse
import json
import re
from pathlib import Path
from typing import Any, Iterator
from paddleocr import TextRecognition
_LTR_RUN = re.compile(r"[a-zA-Z0-9 :*./%+-]")
def pred_reverse(text: str) -> str:
"""Convert PaddleOCR visual-order Arabic output to logical reading order."""
segments: list[str] = []
current_ltr = ""
for character in text:
if _LTR_RUN.search(character):
current_ltr += character
continue
if current_ltr:
segments.append(current_ltr)
current_ltr = ""
segments.append(character)
if current_ltr:
segments.append(current_ltr)
return "".join(reversed(segments))
class BinaTextRecognition:
def __init__(self, model_dir: str | Path | None = None, device: str | None = None) -> None:
model_dir = Path(model_dir) if model_dir else Path(__file__).resolve().parent / "inference"
options: dict[str, Any] = {"model_dir": str(model_dir)}
if device:
options["device"] = device
self._model = TextRecognition(**options)
def predict(self, inputs: str | Path | list[str] | list[Path], batch_size: int = 1) -> Iterator[dict[str, Any]]:
for result in self._model.predict(input=inputs, batch_size=batch_size):
payload = result.json() if callable(result.json) else result.json
raw = payload["res"]
visual = str(raw["rec_text"])
yield {
"input_path": raw.get("input_path"),
"text": pred_reverse(visual),
"score": float(raw["rec_score"]),
"raw_visual_text": visual,
}
def main() -> int:
parser = argparse.ArgumentParser(description="Recognize Persian text-line crops with Bina 0.2")
parser.add_argument("images", nargs="+")
parser.add_argument("--model-dir", default=str(Path(__file__).resolve().parent / "inference"))
parser.add_argument("--device", default="cpu", help="cpu, gpu:0, ...")
parser.add_argument("--batch-size", type=int, default=1)
args = parser.parse_args()
model = BinaTextRecognition(args.model_dir, device=args.device)
for prediction in model.predict(args.images, batch_size=args.batch_size):
print(json.dumps(prediction, ensure_ascii=False))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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