Complete self-contained Bina 0.2 Rizeh release and quick start

LICENSE

@@ -0,0 +1,201 @@
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MODEL_CARD_METRICS.json

@@ -1,7 +1,9 @@
 {
   "model": "Bina 0.2 Rizeh",
   "repo": "Reza2kn/Bina-0.2-Rizeh",
+  "parameters": 35578883,
   "benchmark": "Reza2kn/persian-ocr-double-benchmark",
+  "dataset_revision": "fefe25215114da8ac0ea21ff5e8f97204b2762d5",
   "rows_per_split": 6669,
   "missing_predictions": 0,
   "splits": {
@@ -26,5 +28,10 @@
     "s3": 11.7764,
     "essential_error": 13.4171,
     "triple_threat": 11.4661
+  },
+  "triple_threat_weights": {
+    "wer": 0.2,
+    "cer": 0.2,
+    "s3": 0.6
   }
 }

MODEL_PROVENANCE.json

@@ -0,0 +1,61 @@
+{
+  "schema": "bina02.ppocrv6.public-release.v2",
+  "model_id": "Reza2kn/Bina-0.2-Rizeh",
+  "public_name": "Bina 0.2 Rizeh",
+  "family_role": "Medium",
+  "architecture": "PP-OCRv6_medium_rec",
+  "parameters": 35578883,
+  "teacher": null,
+  "source_release": "stage8-gate42-20260726",
+  "training": {
+    "stage8_mix_rows": 2198483,
+    "hard_label_loss": "CTC",
+    "teacher_logit_distillation": false,
+    "eval_linked_checkpoint_selection": true,
+    "early_stop_triggered": false,
+    "best_handwriting_norm_edit_dis": 0.9671727329321572,
+    "best_printed_norm_edit_dis": 0.9071053593763533
+  },
+  "benchmark": {
+    "model": "Bina 0.2 Rizeh",
+    "repo": "Reza2kn/Bina-0.2-Rizeh",
+    "parameters": 35578883,
+    "benchmark": "Reza2kn/persian-ocr-double-benchmark",
+    "dataset_revision": "fefe25215114da8ac0ea21ff5e8f97204b2762d5",
+    "rows_per_split": 6669,
+    "missing_predictions": 0,
+    "splits": {
+      "handwriting": {
+        "wer": 19.0833,
+        "cer": 13.6399,
+        "s3": 13.7015,
+        "essential_error": 14.7567,
+        "triple_threat": 14.7656
+      },
+      "printed": {
+        "wer": 8.9257,
+        "cer": 2.3535,
+        "s3": 9.8512,
+        "essential_error": 12.0774,
+        "triple_threat": 8.1666
+      }
+    },
+    "macro_equal_split": {
+      "wer": 14.0045,
+      "cer": 7.9967,
+      "s3": 11.7764,
+      "essential_error": 13.4171,
+      "triple_threat": 11.4661
+    },
+    "triple_threat_weights": {
+      "wer": 0.2,
+      "cer": 0.2,
+      "s3": 0.6
+    }
+  },
+  "bundled_detector": {
+    "repo": "PaddlePaddle/PP-OCRv6_medium_det",
+    "revision": "8e0f56fb2ef86b461d99cfc7ac5c137738985f61",
+    "license": "Apache-2.0"
+  }
+}

QUICKSTART.md

@@ -0,0 +1,50 @@
+# Bina 0.2 Rizeh: quick start / راه‌اندازی سریع
+
+This repository includes both the Persian recognizer and the PP-OCRv6 Medium detector, so the commands below do not download model weights at runtime.
+
+این مخزن هم مدل تشخیص متن فارسی و هم آشکارساز PP-OCRv6 Medium را دارد؛ بنابراین هنگام اجرا وزن دیگری دانلود نمی‌شود.
+
+## Install / نصب
+
+```bash
+git lfs install
+git clone https://huggingface.co/Reza2kn/Bina-0.2-Rizeh
+cd Bina-0.2-Rizeh
+python -m pip install "paddlepaddle>=3.2,<4" "paddleocr>=3.3,<4"
+```
+
+The command above installs CPU PaddlePaddle. For NVIDIA GPUs, install the PaddlePaddle GPU wheel matching your CUDA version first, then install `paddleocr>=3.3,<4`.
+
+دستور بالا نسخه CPU را نصب می‌کند. برای کارت NVIDIA ابتدا نسخه GPU سازگار با CUDA خود را نصب کنید و سپس PaddleOCR را نصب کنید.
+
+## Full page / صفحه کامل
+
+```bash
+python bina_page_ocr.py page.jpg --device cpu --output result.json
+# NVIDIA example:
+python bina_page_ocr.py page.jpg --device gpu:0 --output result.json
+```
+
+Images and PDFs are accepted. Output is UTF-8 JSON containing page text, logical-order Persian lines, confidence scores, and boxes.
+
+## Line crops / برش خط
+
+```bash
+python bina_text_recognition.py line.jpg --device cpu
+```
+
+Python:
+
+```python
+from bina_text_recognition import BinaTextRecognition
+model = BinaTextRecognition(device="cpu")
+for row in model.predict("line.jpg"):
+    print(row["text"], row["score"])
+```
+
+## Paths / مسیرها
+
+- Recognizer: `inference/`
+- Bundled detector: `detector/`
+- Best training checkpoint: `checkpoint/`
+- Exact benchmark evidence: `benchmark/`

README.md

@@ -8,6 +8,7 @@ tags:
 - ocr
 - persian
 - handwriting
+- printed-text
 - paddleocr
 - pp-ocrv6
 - ctc
@@ -18,25 +19,43 @@ datasets:
 
 # Bina 0.2 Rizeh
 
-Bina 0.2 Rizeh is a compact Persian printed and handwriting recognizer fine-tuned from the
-`PP-OCRv6_medium_rec` architecture. It uses PaddleOCR's CTC recognition head and ships both the
-exported inference model and the resumable training checkpoint from the Stage-8 release.
+Bina 0.2 Rizeh is the **Medium** member of the Bina 0.2 Persian OCR family. It uses the `PP-OCRv6_medium_rec` CTC recognition architecture and contains **35,578,883 parameters (35.579M)**. The model targets Persian handwriting and printed text; preserving unrelated-language quality was not a training goal.
 
-## Important model boundary
+This is a complete recognizer release, not a loose checkpoint: exported inference graph and weights, preprocessing/postprocessing configuration with the embedded Persian character dictionary, best training checkpoint, benchmark provenance, portable logical-order inference wrappers, and an offline full-page detector are included.
 
-The neural checkpoint recognizes **text-line or text-region crops**. It is not, by itself, a
-standalone full-page detector. The full-page benchmark below used the included adaptive75
-**two-layer pipeline**: page-region detection and template isolation, CTC recognition of the
-remaining regions, then reading-order assembly. The scripts used for that exact run are preserved
-under `pipeline/` and the benchmark records are under `benchmark/`.
+## Quick run
 
-## Persian OCR Triple Threat results
+```bash
+git lfs install
+git clone https://huggingface.co/Reza2kn/Bina-0.2-Rizeh
+cd Bina-0.2-Rizeh
+python -m pip install "paddlepaddle>=3.2,<4" "paddleocr>=3.3,<4"
+python bina_page_ocr.py page.jpg --device cpu --output result.json
+```
+
+For a pre-cropped text line:
+
+```bash
+python bina_text_recognition.py line.jpg --device cpu
+```
+
+See [`QUICKSTART.md`](./QUICKSTART.md) for CPU, NVIDIA, Python, image, and PDF examples in English and Persian.
+
+## What is self-contained?
 
-All 13,338 frozen benchmark rows were scored, with zero missing predictions. Lower is better.
-Triple Threat is `20% WER + 20% CER + 60% S3`; Overall is a 50/50 macro-average of the printed and
-handwriting splits.
+- `inference/` is the exported Bina text recognizer.
+- `detector/` is the pinned official `PaddlePaddle/PP-OCRv6_medium_det` release used for portable full-page OCR.
+- `bina_text_recognition.py` performs logical-order Persian line recognition.
+- `bina_page_ocr.py` detects, recognizes, orders, and emits full-page JSON without downloading model weights.
+- `checkpoint/`, `benchmark/`, `MODEL_PROVENANCE.json`, and `SHA256SUMS` make the release auditable and reusable for continued fine-tuning.
 
-| Split | Pages | WER | CER | S3 | Essential error | Triple Threat |
+The frozen benchmark used a more specialized `adaptive75` two-layer form/template pipeline. The portable full-page CLI is designed for general pages and is not claimed to reproduce that specialized layout isolation exactly.
+
+## Persian OCR Triple Threat
+
+All models were evaluated on the same frozen 6,669 printed + 6,669 handwriting rows, with zero missing predictions. Lower is better. Triple Threat is `0.2 × WER + 0.2 × CER + 0.6 × S³`; overall is the equal split macro-average.
+
+| Split | Rows | WER | CER | S³ | Essential error | Triple Threat |
 |---|---:|---:|---:|---:|---:|---:|
 | Handwriting | 6,669 | 19.0833 | 13.6399 | 13.7015 | 14.7567 | 14.7656 |
 | Printed | 6,669 | 8.9257 | 2.3535 | 9.8512 | 12.0774 | 8.1666 |
@@ -44,58 +63,45 @@ handwriting splits.
 
 Leaderboard: [Persian OCR Triple Threat](https://huggingface.co/spaces/Reza2kn/PersianOCR-TripleThreat)
 
-The release did **not** reach the single-digit handwriting gate. Printed Triple Threat is already
-single digit; handwriting remains the principal limitation.
+## Training and provenance
 
-## Training snapshot
+- Architecture: `PP-OCRv6_medium_rec`.
+- Public parameter count: `35,578,883`.
+- Stage-8 recognition mix: 2,198,483 rows drawn from Persian handwriting, printed OCR, and native/form replay.
+- Best held-out normalized edit similarity: handwriting `0.9671727`, printed `0.9071054`.
+- Eval-linked checkpoint selection was active. The schedule ended while the selected checkpoint was still improving; no divergence stop fired.
+- Medium training used hard-label CTC and serves as the distillation teacher for the smaller siblings.
+- Frozen benchmark revision: `fefe25215114da8ac0ea21ff5e8f97204b2762d5`.
+- Frozen detection SHA-256: `836044bd3d6dc4c2a66cb8a301e5bf86e48e305a7463ec92eec98f656f46e132`.
+- Internal source release: `stage8-gate42-20260726`.
 
-- Stage-8 training mix: 2,198,483 recognition crops.
-- Mix composition: 1,000,000 clean handwriting, 1,000,000 printed, and 198,483 native/form crops.
-- Best handwriting validation normalized edit distance: 0.9671727.
-- Best printed validation normalized edit distance: 0.9071054.
-- Optimizer state was preserved from Stage 4.
-- The planned Stage-8 schedule completed without an overfitting stop signal.
+## Input and output contract
 
-The source pages came from
-[`Reza2kn/persian-handwriting-pages-3.69m`](https://huggingface.co/datasets/Reza2kn/persian-handwriting-pages-3.69m)
-and
-[`Reza2kn/persian-printed-ocr-3.5m`](https://huggingface.co/datasets/Reza2kn/persian-printed-ocr-3.5m),
-with line/region preparation and native form replay described by `data/MIX_MANIFEST.json`.
+The recognizer works best on horizontal, content-tight line crops. Its exported `inference.yml` is authoritative for resize/padding and the 161-symbol dictionary. The wrappers convert PaddleOCR visual-order Arabic output to logical Persian order while preserving Latin/numeric runs.
 
-## Inference artifacts
+The full-page CLI emits JSON with assembled text plus every recognized line, box, confidence score, and raw visual-order output. For complex forms, grids, multi-column layouts, or exact benchmark reproduction, use the preserved specialized pipeline and layout stages rather than assuming generic detection has perfect reading order.
 
-The exported Paddle model is available both at repository root and under `inference/`:
+## Limitations
 
-- `inference.json`
-- `inference.pdiparams`
-- `inference.yml`
+- Persian-focused; other languages were not preserved or benchmarked.
+- Handwriting remains harder than printed text, especially for the smaller siblings.
+- Generic page detection may need layout-specific reading-order logic for forms and multi-column documents.
+- Confidence scores are model scores, not calibrated probabilities.
+- The benchmark measures the bundled family pipeline and should not be interpreted as universal document accuracy.
 
-With PaddleOCR 3.x, a recognition crop can be run with the custom model directory:
+## فارسی
 
-```python
-from paddleocr import TextRecognition
+Bina 0.2 Rizeh عضو **متوسط** خانواده OCR فارسی Bina 0.2 است و 35.579 میلیون پارامتر دارد. این مخزن فقط یک checkpoint نیست: مدل inference، تنظیمات و واژه‌نامه کاراکتری، checkpoint آموزش، آشکارساز صفحه، اسکریپت خط و صفحه کامل، نتایج دقیق benchmark و checksumها همگی داخل مخزن هستند.
 
-recognizer = TextRecognition(model_dir="./inference")
-for result in recognizer.predict(input="persian-line.png", batch_size=1):
-    result.print()
-```
-
-For page OCR, use a detector/layout stage first and pass its ordered text crops to this recognizer.
-The bundled `pipeline/` scripts record the exact full-page benchmark workflow; their original
-absolute cluster paths must be adapted to a new machine.
+اجرای سریع CPU:
 
-## Repository layout
+```bash
+python -m pip install "paddlepaddle>=3.2,<4" "paddleocr>=3.3,<4"
+python bina_page_ocr.py page.jpg --device cpu --output result.json
+```
 
-- `inference/`: exported Paddle inference graph, parameters, and configuration.
-- `checkpoint/`: resumable best checkpoint, optimizer state, configs, and eval-gate state.
-- `pipeline/`: detection, recognition, assembly, benchmark, and launch scripts.
-- `benchmark/`: exact split metrics, logs, and run summaries.
-- `data/MIX_MANIFEST.json`: exact Stage-8 train/eval composition.
-- `RELEASE.json`: immutable release identity and headline metrics.
-- `SHA256SUMS`: checksums for the verified packaged release.
+برای GPU نسخه PaddlePaddle سازگار با CUDA را نصب کنید و `--device gpu:0` بدهید. برای یک خط بریده‌شده از `bina_text_recognition.py` استفاده کنید. خروجی فارسی به ترتیب منطقی خواندن برگردانده می‌شود. جزئیات بیشتر در [`QUICKSTART.md`](./QUICKSTART.md) آمده است.
 
-## Release identity
+## License
 
-This public model name is **Bina 0.2 Rizeh**. The immutable packaging metadata retains the internal
-release name `Bina-0.2-RizehPizeh` and release ID `stage8-gate42-20260726` so it remains traceable to
-the exact trained artifact.
+Apache-2.0. The bundled detector is the official Apache-2.0 `PaddlePaddle/PP-OCRv6_medium_det` artifact pinned in `detector/SOURCE.json`.

RUNTIME_SHA256SUMS

@@ -0,0 +1,16 @@
+b802ea6f182ffb341716b2f649b8f48a2c1f3e4fb6db26f4cfd31771d80cd8b8  inference/inference.json
+f8e1b5b4e9ef11b46c521fed7d1c9b7e7b2d08c4c879aa01353acd7308a6e9fb  inference/inference.pdiparams
+650bfd52635c3a5680479df99a4c5cb3b94e2875b25a7bc3aea45ec1bad6a841  inference/inference.yml
+3840c5c0c61c294264d2dd77b8777be6ddd90121ef4e0e64abcd22edea581d6e  LICENSE
+9fd4a597a64f4f03ec7056f03514c348f3a25d486a4af8ad7d2af526bddedb63  MODEL_CARD_METRICS.json
+4704a89e5a5f3ee4ce19f1f1e6050f801efb4c12aaad04207ac2ccf58042090b  MODEL_PROVENANCE.json
+13d7558ea9a87a32d5c314036ca0d3ddccde58f09d59cead52841fbbf9be1a2a  QUICKSTART.md
+a146386868a471ed61f3f2914ee201f0212b93bd1271a5c838c523e6ebb127ac  README.md
+19d0372a591ad86dfd22ddac8f4dc9edae1dae0c120fe536cb1d582626a3027c  THIRD_PARTY_NOTICES.md
+ad52c0d66e4117d2806c5014f37a9bf8a08788df4f5aa1bea05597bb2b680ba0  bina_page_ocr.py
+b054cde1e6a8842fe0596f38e6a9e762e4c81249c68071306838948a21699e0c  bina_text_recognition.py
+cfef0ea9ea896b13d5c433b38dbb48db184bd0f35365120194afd686f278ac58  requirements.txt
+8ceccaab12056908688a7f31fd28d9efd9f2334a7dccb30df25aa7ca84bdf305  detector/SOURCE.json
+0f1a7ec35da36173529c7a60238b7f7919e3831929c3f700ad90ad4896adecd5  detector/inference.json
+85218d2e3d98f5a21c58b4220627be923a97aee5db3cc71f39536ab31ac53960  detector/inference.pdiparams
+7298d5ead546584af2504d03355f881ac7a7bc0eb1e282d3e159277c1d0af871  detector/inference.yml

THIRD_PARTY_NOTICES.md

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+# Third-party notices
+
+The bundled `detector/` artifact is copied without modification from
+[`PaddlePaddle/PP-OCRv6_medium_det`](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_det) at revision
+`8e0f56fb2ef86b461d99cfc7ac5c137738985f61`. PaddleOCR and that model are distributed under Apache-2.0.

bina_page_ocr.py

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+#!/usr/bin/env python3
+"""Offline full-page Persian OCR using bundled detector plus a Bina recognizer."""
+from __future__ import annotations
+import argparse
+import json
+import re
+from pathlib import Path
+from typing import Any, Iterator
+from paddleocr import PaddleOCR
+
+_LTR_RUN = re.compile(r"[a-zA-Z0-9 :*./%+-]")
+
+def pred_reverse(text: str) -> str:
+    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))
+
+def _json_payload(result: Any) -> dict[str, Any]:
+    payload = result.json() if callable(result.json) else result.json
+    return payload.get("res", payload)
+
+def _box_list(box: Any) -> list[float]:
+    if hasattr(box, "tolist"):
+        box = box.tolist()
+    return [float(value) for value in box]
+
+def ordered_lines(payload: dict[str, Any]) -> list[dict[str, Any]]:
+    visual_texts = [str(value) for value in payload.get("rec_texts", [])]
+    texts = [pred_reverse(value) for value in visual_texts]
+    scores = [float(value) for value in payload.get("rec_scores", [0.0] * len(texts))]
+    boxes = payload.get("rec_boxes", [])
+    if len(boxes) != len(texts):
+        return [{"text": text, "score": scores[i], "raw_visual_text": visual_texts[i]} for i, text in enumerate(texts)]
+    items = []
+    for i, (text, box) in enumerate(zip(texts, boxes)):
+        x0, y0, x1, y1 = _box_list(box)
+        items.append({
+            "text": text,
+            "score": scores[i],
+            "raw_visual_text": visual_texts[i],
+            "box": [x0, y0, x1, y1],
+            "x": (x0 + x1) / 2,
+            "y": (y0 + y1) / 2,
+            "height": max(y1 - y0, 1.0),
+        })
+    items.sort(key=lambda item: item["y"])
+    rows: list[list[dict[str, Any]]] = []
+    for item in items:
+        if not rows:
+            rows.append([item])
+            continue
+        row = rows[-1]
+        mean_y = sum(part["y"] for part in row) / len(row)
+        mean_h = sum(part["height"] for part in row) / len(row)
+        if abs(item["y"] - mean_y) <= 0.55 * max(item["height"], mean_h):
+            row.append(item)
+        else:
+            rows.append([item])
+    output = []
+    for row_index, row in enumerate(rows):
+        row.sort(key=lambda item: item["x"], reverse=True)
+        for item in row:
+            output.append({key: value for key, value in item.items() if key not in {"x", "y", "height"}} | {"row": row_index})
+    return output
+
+class BinaPageOCR:
+    def __init__(self, model_dir: str | Path | None = None, detector_dir: str | Path | None = None,
+                 device: str | None = None, score_threshold: float = 0.0) -> None:
+        base = Path(__file__).resolve().parent
+        model_dir = Path(model_dir) if model_dir else base / "inference"
+        detector_dir = Path(detector_dir) if detector_dir else base / "detector"
+        options: dict[str, Any] = {
+            "text_detection_model_dir": str(detector_dir),
+            "text_recognition_model_dir": str(model_dir),
+            "use_doc_orientation_classify": False,
+            "use_doc_unwarping": False,
+            "use_textline_orientation": False,
+            "text_rec_score_thresh": score_threshold,
+        }
+        if device:
+            options["device"] = device
+        self._ocr = PaddleOCR(**options)
+
+    def predict(self, inputs: list[str | Path]) -> Iterator[dict[str, Any]]:
+        for source in inputs:
+            for page_index, result in enumerate(self._ocr.predict(str(source))):
+                raw = _json_payload(result)
+                lines = ordered_lines(raw)
+                row_text: dict[int, list[str]] = {}
+                for line in lines:
+                    row_text.setdefault(int(line.get("row", len(row_text))), []).append(line["text"])
+                text = "\n".join(" ".join(row_text[index]).strip() for index in sorted(row_text) if row_text[index])
+                yield {"input_path": str(source), "page_index": page_index, "text": text, "lines": lines}
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Run self-contained full-page Persian OCR with Bina 0.2")
+    parser.add_argument("inputs", nargs="+")
+    parser.add_argument("--model-dir", default=str(Path(__file__).resolve().parent / "inference"))
+    parser.add_argument("--detector-dir", default=str(Path(__file__).resolve().parent / "detector"))
+    parser.add_argument("--device", default="cpu", help="cpu, gpu:0, ...")
+    parser.add_argument("--score-threshold", type=float, default=0.0)
+    parser.add_argument("--output", type=Path)
+    args = parser.parse_args()
+    model = BinaPageOCR(args.model_dir, args.detector_dir, args.device, args.score_threshold)
+    predictions = list(model.predict(args.inputs))
+    for prediction in predictions:
+        print(json.dumps(prediction, ensure_ascii=False))
+    if args.output:
+        args.output.write_text(json.dumps(predictions, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+    return 0
+
+if __name__ == "__main__":
+    raise SystemExit(main())

bina_text_recognition.py

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+#!/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())

detector/SOURCE.json

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+{
+  "repo": "PaddlePaddle/PP-OCRv6_medium_det",
+  "revision": "8e0f56fb2ef86b461d99cfc7ac5c137738985f61",
+  "license": "Apache-2.0",
+  "purpose": "Bundled full-page text detector used by bina_page_ocr.py"
+}

detector/inference.pdiparams

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+version https://git-lfs.github.com/spec/v1
+oid sha256:85218d2e3d98f5a21c58b4220627be923a97aee5db3cc71f39536ab31ac53960
+size 61960476

detector/inference.yml

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+Global:
+  model_name: PP-OCRv6_medium_det
+Hpi:
+  backend_configs:
+    paddle_infer:
+      trt_dynamic_shapes: &id001
+        x:
+        - - 1
+          - 3
+          - 32
+          - 32
+        - - 1
+          - 3
+          - 736
+          - 736
+        - - 1
+          - 3
+          - 4000
+          - 4000
+    tensorrt:
+      dynamic_shapes: *id001
+PostProcess:
+  box_thresh: 0.45
+  max_candidates: 3000
+  name: DBPostProcess
+  thresh: 0.2
+  unclip_ratio: 1.4
+PreProcess:
+  transform_ops:
+  - DecodeImage:
+      channel_first: false
+      img_mode: BGR
+  - DetLabelEncode: null
+  - DetResizeForTest: null
+  - NormalizeImage:
+      mean:
+      - 0.485
+      - 0.456
+      - 0.406
+      order: hwc
+      scale: 1./255.
+      std:
+      - 0.229
+      - 0.224
+      - 0.225
+  - ToCHWImage: null
+  - KeepKeys:
+      keep_keys:
+      - image
+      - shape
+      - polys
+      - ignore_tags

requirements.txt

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+paddleocr>=3.3.0,<4