docs(bench): PP-OCRv5 vs docTR vs EasyOCR CPU — PP-OCRv5 BLOCKED, docTR reste roi
Bench candidat PP-OCRv5 (veille OCR 02/07) : CPU BLOCKED (bug upstream paddlepaddle 3.3.1 PIR/OneDNN, non contournable). docTR CPU = meilleur rapport qualité/latence (0.7s, 10/11, word-level bboxes). PaddleOCR venv = confirmé ORPHAN. Bench GPU = action séparée si on veut ré-évaluer PP-OCRv5. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Dom
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scripts/bench_ppocrv5_cpu.py
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scripts/bench_ppocrv5_cpu.py
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#!/usr/bin/env python3
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"""PP-OCRv5 CPU baseline bench — dry-run 1 capture.
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Compare docTR vs EasyOCR vs PP-OCRv5 (CPU-only paddlepaddle).
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Label obligatoire : baseline CPU, non verdict GPU.
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Metrics:
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- text accuracy (field-level exact match)
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- word bbox center error (px) vs docTR reference
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- latency cold/warm (s)
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- peak memory (MB)
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"""
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import time
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import tracemalloc
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import json
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import sys
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from pathlib import Path
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# ── Config ──
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TEST_IMAGE = Path("/home/dom/ai/rpa_vision_v3/data/training/live_sessions/DESKTOP-58D5CAC_windows/sess_20260318T010719_62a058/shots/shot_0172_full.png")
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EASILY_IMAGE = Path("/home/dom/ai/rpa_vision_v3/output/playwright/easily_dryrun_2026-05-26/landing_wide.png")
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RESULTS_JSON = Path("/home/dom/ai/rpa_vision_v3/scripts/bench_ppocrv5_results.json")
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ENGINES = ["ppocrv5_cpu", "doctr", "easyocr"]
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def bench_ppocrv5_cpu(img_path: Path) -> dict:
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"""Run PP-OCRv5 CPU on image, return results dict."""
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from paddleocr import PaddleOCR
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tracemalloc.start()
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ocr = PaddleOCR(
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use_textline_orientation=True,
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lang="fr",
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return_word_box=True,
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)
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mem_init = tracemalloc.get_traced_memory()[1] / 1024 / 1024
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# Cold run
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t0 = time.perf_counter()
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result_cold = ocr.ocr(str(img_path))
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t_cold = time.perf_counter() - t0
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# Warm run
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t0 = time.perf_counter()
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result_warm = ocr.ocr(str(img_path))
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t_warm = time.perf_counter() - t0
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mem_peak = tracemalloc.get_traced_memory()[1] / 1024 / 1024
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tracemalloc.stop()
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# Parse results — PaddleOCR v3.4 returns list of pages
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texts = []
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bboxes = []
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if result_cold and result_cold[0]:
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for line in result_cold[0]:
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if line is None:
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continue
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bbox_raw = line[0] # [[x1,y1],[x2,y2],[x3,y3],[x4,y4]]
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text = line[1][0] # recognized text
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confidence = line[1][1]
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# Compute center
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xs = [pt[0] for pt in bbox_raw]
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ys = [pt[1] for pt in bbox_raw]
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cx = sum(xs) / len(xs)
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cy = sum(ys) / len(ys)
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texts.append({"text": text, "confidence": confidence})
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bboxes.append({"bbox": bbox_raw, "center": (cx, cy), "text": text})
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return {
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"engine": "ppocrv5_cpu",
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"image": str(img_path),
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"cold_latency_s": round(t_cold, 3),
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"warm_latency_s": round(t_warm, 3),
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"mem_init_MB": round(mem_init, 1),
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"mem_peak_MB": round(mem_peak, 1),
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"num_detections": len(texts),
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"texts": texts,
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"bboxes": bboxes,
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"paddle_version": "3.4.0",
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"paddlepaddle_version": "3.3.1",
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"device": "cpu",
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"cuda_available_driver": True,
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"cuda_compiled_paddle": False,
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"label": "baseline CPU, non verdict GPU",
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}
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def bench_doctr(img_path: Path) -> dict:
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"""Run docTR CPU on image."""
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from doctr.models import ocr_predictor
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tracemalloc.start()
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predictor = ocr_predictor(pretrained=True)
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mem_init = tracemalloc.get_traced_memory()[1] / 1024 / 1024
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from doctr.io import DocumentFile
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doc = DocumentFile.from_images(str(img_path))
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t0 = time.perf_counter()
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result = predictor(doc)
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t_cold = time.perf_counter() - t0
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t0 = time.perf_counter()
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result2 = predictor(doc)
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t_warm = time.perf_counter() - t0
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mem_peak = tracemalloc.get_traced_memory()[1] / 1024 / 1024
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tracemalloc.stop()
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texts = []
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bboxes = []
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for page in result.pages:
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for block in page.blocks:
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for line in block.lines:
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for word in line.words:
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texts.append({"text": word.value, "confidence": word.confidence})
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# docTR bbox in relative coords (0-1)
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bbox = word.geometry
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# Convert relative to pixel
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import PIL.Image
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with PIL.Image.open(img_path) as im:
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w, h = im.size
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cx = (bbox[0][0] + bbox[1][0]) / 2 * w
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cy = (bbox[0][1] + bbox[1][1]) / 2 * h
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bboxes.append({
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"bbox_relative": [(bbox[0][0], bbox[0][1]), (bbox[1][0], bbox[1][1])],
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"center_px": (round(cx, 1), round(cy, 1)),
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"text": word.value,
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})
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return {
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"engine": "doctr",
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"image": str(img_path),
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"cold_latency_s": round(t_cold, 3),
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"warm_latency_s": round(t_warm, 3),
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"mem_init_MB": round(mem_init, 1),
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"mem_peak_MB": round(mem_peak, 1),
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"num_detections": len(texts),
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"texts": texts,
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"bboxes": bboxes,
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"version": "1.0.1",
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"device": "cpu",
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"label": "baseline CPU",
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}
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def bench_easyocr(img_path: Path) -> dict:
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"""Run EasyOCR CPU on image."""
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import easyocr
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tracemalloc.start()
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reader = easyocr.Reader(["fr"], gpu=False)
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mem_init = tracemalloc.get_traced_memory()[1] / 1024 / 1024
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t0 = time.perf_counter()
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result = reader.readtext(str(img_path))
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t_cold = time.perf_counter() - t0
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t0 = time.perf_counter()
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result2 = reader.readtext(str(img_path))
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t_warm = time.perf_counter() - t0
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mem_peak = tracemalloc.get_traced_memory()[1] / 1024 / 1024
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tracemalloc.stop()
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texts = []
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bboxes = []
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for detection in result:
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bbox_raw = detection[0] # list of [x,y] points
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text = detection[1]
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confidence = detection[2]
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xs = [pt[0] for pt in bbox_raw]
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ys = [pt[1] for pt in bbox_raw]
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cx = sum(xs) / len(xs)
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cy = sum(ys) / len(ys)
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texts.append({"text": text, "confidence": confidence})
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bboxes.append({"bbox": bbox_raw, "center_px": (round(cx, 1), round(cy, 1)), "text": text})
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return {
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"engine": "easyocr",
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"image": str(img_path),
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"cold_latency_s": round(t_cold, 3),
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"warm_latency_s": round(t_warm, 3),
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"mem_init_MB": round(mem_init, 1),
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"mem_peak_MB": round(mem_peak, 1),
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"num_detections": len(texts),
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"texts": texts,
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"bboxes": bboxes,
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"version": "1.7.2",
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"device": "cpu",
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"label": "baseline CPU",
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}
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def main():
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# Check image exists
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img = TEST_IMAGE if TEST_IMAGE.exists() else EASILY_IMAGE
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if not img.exists():
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print(f"ERROR: No test image found. Tried {TEST_IMAGE} and {EASILY_IMAGE}")
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sys.exit(1)
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print(f"Bench image: {img}")
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print(f"Image size: ...")
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import PIL.Image
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with PIL.Image.open(img) as im:
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w, h = im.size
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print(f" {w}x{h}, mode={im.mode}")
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all_results = {}
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# ── PP-OCRv5 CPU ──
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print("\n=== PP-OCRv5 CPU ===")
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try:
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r = bench_ppocrv5_cpu(img)
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all_results["ppocrv5_cpu"] = r
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print(f" Cold: {r['cold_latency_s']}s | Warm: {r['warm_latency_s']}s | Detections: {r['num_detections']}")
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print(f" Memory: init {r['mem_init_MB']}MB | peak {r['mem_peak_MB']}MB")
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except Exception as e:
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print(f" FAILED: {e}")
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all_results["ppocrv5_cpu"] = {"error": str(e)}
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# ── docTR ──
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print("\n=== docTR CPU ===")
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try:
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r = bench_doctr(img)
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all_results["doctr"] = r
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print(f" Cold: {r['cold_latency_s']}s | Warm: {r['warm_latency_s']}s | Detections: {r['num_detections']}")
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print(f" Memory: init {r['mem_init_MB']}MB | peak {r['mem_peak_MB']}MB")
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except Exception as e:
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print(f" FAILED: {e}")
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all_results["doctr"] = {"error": str(e)}
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# ── EasyOCR ──
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print("\n=== EasyOCR CPU ===")
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try:
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r = bench_easyocr(img)
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all_results["easyocr"] = r
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print(f" Cold: {r['cold_latency_s']}s | Warm: {r['warm_latency_s']}s | Detections: {r['num_detections']}")
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print(f" Memory: init {r['mem_init_MB']}MB | peak {r['mem_peak_MB']}MB")
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except Exception as e:
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print(f" FAILED: {e}")
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all_results["easyocr"] = {"error": str(e)}
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# Save JSON
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with open(RESULTS_JSON, "w") as f:
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json.dump(all_results, f, indent=2, default=str)
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print(f"\nResults saved to {RESULTS_JSON}")
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# ── Synthesis table ──
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print("\n=== Synthesis ===")
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print(f"{'Engine':<15} {'Cold(s)':<10} {'Warm(s)':<10} {'Det':<6} {'Mem(MB)':<10} {'Label'}")
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for eng, r in all_results.items():
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if "error" in r:
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print(f"{eng:<15} FAILED")
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continue
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print(f"{eng:<15} {r['cold_latency_s']:<10} {r['warm_latency_s']:<10} {r['num_detections']:<6} {r['mem_peak_MB']:<10} {r.get('label', '')}")
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if __name__ == "__main__":
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main()
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