RapidOCR支持TensorRT推理引擎

记录支持TensorRT推理引擎的过程文档。

引言¶

首先，衷心感谢 GitHub 用户 @LocNgoXuan23 提交的 PR #623！正是有了这项贡献，我们才得以顺利推进 TensorRT 作为 RapidOCR 的推理引擎支持。

该 PR 已经实现了非常完善的 TensorRT 集成，不仅包含完整的推理逻辑，还附带了单元测试和详尽的性能基准（benchmark）数据。我所做的主要是通读代码，理解其实现机制。

起初，我以为 TensorRT 可以像其他推理引擎（如 ONNX Runtime 或 MNN）一样，将 ONNX 模型预先转换为通用的目标格式后直接分发使用。但在深入阅读源码后，我才意识到事情并非如此简单：TensorRT 的 .engine 模型文件是与具体硬件强绑定的，必须根据当前设备的 GPU 架构（如 compute capability）动态生成。

因此，在实际使用中，用户首次指定 TensorRT 作为推理引擎时，程序会自动触发 .engine 文件的构建流程。这一过程的耗时取决于所用设备的性能——通常在桌面级或服务器级 GPU 上较快，在边缘设备（如 Jetson）上则可能稍长。

为了便于横向对比，我在下方整理了 @LocNgoXuan23 在 PR 中提供的测试结果，并额外补充了 硬件设备 一列，方便大家结合自身环境参考性能表现。

运行环境¶¶

Docker镜像：@LocNgoXuan23 在 Discord 中给出的镜像：7.0-gc-triton-devel
设备配置：8 CPU / 256 GB
NVIDIA环境：(详细参见：link )
- cuda: 12.2
- tensorrt: 8.6.1
- cuda-python: 12.2.0
Python环境（3.10.0）：

rapidocr==3.5.0
text_det_metric==0.0.8
text_rec_metric==0.0.1
datasets==3.6.0
onnxruntime==1.23.2

@LocNgoXuan23 运行环境：

GPU: NVIDIA GeForce RTX 3060 (12GB, Compute Capability 8.6)
Driver: 570.211.01
TensorRT: 8.6.1
Python: 3.10.12
ONNX Runtime: 1.18.0

支持Det模型¶

比较转化前后推理精度差异¶

这里主要采用 @LocNgoXuan23 给出的 benchmark 脚本来批量测试。下面是测试单个模型的脚本。

(Exp1) RapidOCR + ONNX Runtime(Exp2) RapidOCR + TensorRT + FP16(Exp3) RapidOCR + TensorRT + FP32

# Step 1: 获得推理结果
import cv2
import numpy as np
from datasets import load_dataset
from tqdm import tqdm

from rapidocr import RapidOCR

engine = RapidOCR()

dataset = load_dataset("SWHL/text_det_test_dataset")
test_data = dataset["test"]

content = []
for i, one_data in enumerate(tqdm(test_data)):
    img = np.array(one_data.get("image"))
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    ocr_results = engine(img, use_det=True, use_cls=False, use_rec=False)
    dt_boxes = ocr_results.boxes

    dt_boxes = [] if dt_boxes is None else dt_boxes.tolist()
    elapse = ocr_results.elapse

    gt_boxes = [v["points"] for v in one_data["shapes"]]
    content.append(f"{dt_boxes}\t{gt_boxes}\t{elapse}")

with open("pred.txt", "w", encoding="utf-8") as f:
    for v in content:
        f.write(f"{v}\n")

# Step 2: 计算指标
from text_det_metric import TextDetMetric

metric = TextDetMetric()
pred_path = "pred.txt"
metric = metric(pred_path)
print(metric)

# Step 1: 获得推理结果
import cv2
import numpy as np
from datasets import load_dataset
from tqdm import tqdm

from rapidocr import EngineType, LangDet, ModelType, OCRVersion, RapidOCR

engine_config = {
    "EngineConfig.tensorrt.use_fp16": True,
    "Det.engine_type": EngineType.TENSORRT,
    "Det.lang_type": LangDet.CH,
    "Det.model_type": ModelType.MOBILE,
    "Det.ocr_version": OCRVersion.PPOCRV4,
    "Det.model_path": "rapidocr/models/ch_PP-OCRv4_det_infer.onnx",
}
engine = RapidOCR(params=engine_config)

dataset = load_dataset("SWHL/text_det_test_dataset")
test_data = dataset["test"]

content = []
for i, one_data in enumerate(tqdm(test_data)):
    img = np.array(one_data.get("image"))
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    ocr_results = engine(img, use_det=True, use_cls=False, use_rec=False)
    dt_boxes = ocr_results.boxes

    dt_boxes = [] if dt_boxes is None else dt_boxes.tolist()
    elapse = ocr_results.elapse

    gt_boxes = [v["points"] for v in one_data["shapes"]]
    content.append(f"{dt_boxes}\t{gt_boxes}\t{elapse}")

with open("pred.txt", "w", encoding="utf-8") as f:
    for v in content:
        f.write(f"{v}\n")

# Step 2: 计算指标
from text_det_metric import TextDetMetric

metric = TextDetMetric()
pred_path = "pred.txt"
metric = metric(pred_path)
print(metric)

# Step 1: 获得推理结果
import cv2
import numpy as np
from datasets import load_dataset
from tqdm import tqdm

from rapidocr import EngineType, LangDet, ModelType, OCRVersion, RapidOCR

engine_config = {
    "EngineConfig.tensorrt.use_fp16": False,
    "Det.engine_type": EngineType.TENSORRT,
    "Det.lang_type": LangDet.CH,
    "Det.model_type": ModelType.MOBILE,
    "Det.ocr_version": OCRVersion.PPOCRV4,
    "Det.model_path": "rapidocr/models/ch_PP-OCRv4_det_infer.onnx",
}
engine = RapidOCR(params=engine_config)

dataset = load_dataset("SWHL/text_det_test_dataset")
test_data = dataset["test"]

content = []
for i, one_data in enumerate(tqdm(test_data)):
    img = np.array(one_data.get("image"))
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    ocr_results = engine(img, use_det=True, use_cls=False, use_rec=False)
    dt_boxes = ocr_results.boxes

    dt_boxes = [] if dt_boxes is None else dt_boxes.tolist()
    elapse = ocr_results.elapse

    gt_boxes = [v["points"] for v in one_data["shapes"]]
    content.append(f"{dt_boxes}\t{gt_boxes}\t{elapse}")

with open("pred.txt", "w", encoding="utf-8") as f:
    for v in content:
        f.write(f"{v}\n")

# Step 2: 计算指标
from text_det_metric import TextDetMetric

metric = TextDetMetric()
pred_path = "pred.txt"
metric = metric(pred_path)
print(metric)

结果对比¶

Tip

1. 其他版本的模型，我这里就直接给出对比结果了。因为教程都是一样的，仅换了一个模型而已。
2. 下面指标仅作为转换前后，比较模型精度差异使用哈！
3. TensorRT FP32 和 FP16 精度对比。

Exp	模型	推理框架	推理引擎	硬件	Precision↑	Recall↑	H-mean↑	Elapse↓
1	ch_PP-OCRv4_det_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.8595	0.8434	0.8514	1.0932
2	ch_PP-OCRv4_det_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.8677	0.8396	0.8534	0.0402
3	ch_PP-OCRv4_det_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.8667	0.8396	0.8529	0.0406

4	ch_PP-OCRv4_det_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.8595	0.8434	0.8514	0.1044
5	ch_PP-OCRv4_det_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.8685	0.8403	0.8542	0.0288
6	ch_PP-OCRv4_det_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.8677	0.8396	0.8534	0.0266

7	ch_PP-OCRv5_mobile_det	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.7858	0.8266	0.8057	1.1108
8	ch_PP-OCRv5_mobile_det	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.7938	0.8220	0.8077	0.0402
9	ch_PP-OCRv5_mobile_det	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.7943	0.8216	0.8077	0.0415

10	ch_PP-OCRv5_mobile_det	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.7858	0.8266	0.8057	0.1296
11	ch_PP-OCRv5_mobile_det	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.7938	0.8220	0.8077	0.0341
12	ch_PP-OCRv5_mobile_det	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.7950	0.8220	0.8083	0.0333

13	ch_PP-OCRv4_det_server_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.7710	0.8579	0.8121	5.4996
14	ch_PP-OCRv4_det_server_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.7811	0.8545	0.8161	0.0579
15	ch_PP-OCRv4_det_server_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.3689	0.0516	0.0905	0.0415

16	ch_PP-OCRv4_det_server_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.7710	0.8579	0.8121	7.2283
17	ch_PP-OCRv4_det_server_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.7805	0.8545	0.8158	0.1167
18	ch_PP-OCRv4_det_server_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.3757	0.0519	0.0913	0.0184

19	en_PP-OCRv3_det_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.8066	0.8380	0.8220	0.7376
20	en_PP-OCRv3_det_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.8153	0.8346	0.8248	0.04
21	en_PP-OCRv3_det_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.8147	0.8346	0.8245	0.0384

22	en_PP-OCRv3_det_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.8066	0.8380	0.8220	0.0839
23	en_PP-OCRv3_det_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.8144	0.8346	0.8244	0.0283
24	en_PP-OCRv3_det_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.8144	0.8346	0.8244	0.0269

25	Multilingual_PP-OCRv3_det_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.4228	0.6921	0.5249	0.7334
26	Multilingual_PP-OCRv3_det_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.4223	0.6906	0.5241	0.0452
27	Multilingual_PP-OCRv3_det_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.4221	0.6906	0.5240	0.0447

28	Multilingual_PP-OCRv3_det_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.4161	0.6841	0.5175	0.1638
29	Multilingual_PP-OCRv3_det_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.4223	0.6906	0.5241	0.0348
30	Multilingual_PP-OCRv3_det_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.4220	0.6906	0.5239	0.0329

31	ch_PP-OCRv5_server_det	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.7394	0.8442	0.7883	4.7435
32	ch_PP-OCRv5_server_det	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	-	-	-	-
33	ch_PP-OCRv5_server_det	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.4143	0.2059	0.2751	0.0387

34	ch_PP-OCRv5_server_det	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.7394	0.8442	0.7883	5.7514
35	ch_PP-OCRv5_server_det	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.7503	0.8411	0.7931	0.0976
36	ch_PP-OCRv5_server_det	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.4163	0.2070	0.2765	0.0453

在 Exp 15 和 Exp 18 中，模型转为 TensorRT FP16 .engine 格式后，各项指标均有大幅度下降，原因暂未查明，小伙伴们使用时注意。

在 Exp 32 中，我在当前设备上尝试构建对应的 TensorRT .engine 模型时未能成功，问题目前尚未解决。具体的错误日志如下：

[INFO] 2026-02-25 08:10:57,064 [RapidOCR] base.py:22: Using engine_name: tensorrt
[INFO] 2026-02-25 08:10:57,415 [RapidOCR] main.py:519: Building TensorRT engine from rapidocr/models/ch_PP-OCRv5_server_det.onnx
[INFO] 2026-02-25 08:11:03,888 [RapidOCR] engine_builder.py:55: Using FP32 precision
[INFO] 2026-02-25 08:11:03,889 [RapidOCR] engine_builder.py:119: Set optimization profile for x: min=[1, 3, 32, 32], opt=[1, 3, 736, 736], max=[1, 3, 2048, 2048]
[INFO] 2026-02-25 08:11:03,889 [RapidOCR] engine_builder.py:67: Building TensorRT engine (this may take a few minutes)...
[02/25/2026-08:13:28] [TRT] [E] 1: No Myelin Error exists
[02/25/2026-08:13:28] [TRT] [E] 1: [runnerBuilderBase.cpp::buildAndSerializeMyelinGraph::326] Error Code 1: Myelin (No Myelin Error exists)
Traceback (most recent call last):
  File "/xxxxx/RapidOCR/python/test_det.py", line 25, in <module>
    engine = RapidOCR(params=engine_config)
  File "/xxxxx/RapidOCR/python/rapidocr/main.py", line 45, in __init__
    self._initialize(cfg)
  File "/xxxxx/RapidOCR/python/rapidocr/main.py", line 66, in _initialize
    self.text_det = TextDetector(cfg.Det)
  File "/xxxxx/RapidOCR/python/rapidocr/ch_ppocr_det/main.py", line 47, in __init__
    self.session = get_engine(cfg.engine_type)(cfg)
  File "/xxxxx/RapidOCR/python/rapidocr/inference_engine/tensorrt/main.py", line 88, in __init__
    self.engine = self._load_or_build_engine(cfg, engine_path)
  File "/xxxxx/RapidOCR/python/rapidocr/inference_engine/tensorrt/main.py", line 528, in _load_or_build_engine
    return builder.build()
  File "/xxxxx/RapidOCR/python/rapidocr/inference_engine/tensorrt/engine_builder.py", line 71, in build
    raise RuntimeError("Failed to build TensorRT engine")
RuntimeError: Failed to build TensorRT engine

支持Rec模型¶

比较转化前后推理精度差异¶

这里主要采用@LocNgoXuan23给出的 benchmark 脚本来批量测试。下面是测试单个模型的脚本。

(Exp1) RapidOCR + ONNX Runtime(Exp2) RapidOCR + TensorRT + FP16(Exp3) RapidOCR + TensorRT + FP32

# Step 1: 获得推理结果
import cv2
import numpy as np
from datasets import load_dataset
from tqdm import tqdm

from rapidocr import RapidOCR

engine = RapidOCR()

dataset = load_dataset("SWHL/text_det_test_dataset")
test_data = dataset["test"]

content = []
for i, one_data in enumerate(tqdm(test_data)):
    img = np.array(one_data.get("image"))
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    ocr_results = engine(img, use_det=True, use_cls=False, use_rec=False)
    dt_boxes = ocr_results.boxes

    dt_boxes = [] if dt_boxes is None else dt_boxes.tolist()
    elapse = ocr_results.elapse

    gt_boxes = [v["points"] for v in one_data["shapes"]]
    content.append(f"{dt_boxes}\t{gt_boxes}\t{elapse}")

with open("pred.txt", "w", encoding="utf-8") as f:
    for v in content:
        f.write(f"{v}\n")

# Step 2: 计算指标
from text_det_metric import TextDetMetric

metric = TextDetMetric()
pred_path = "pred.txt"
metric = metric(pred_path)
print(metric)

# Step 1: 获得推理结果
import time

import cv2
import numpy as np
from datasets import load_dataset
from tqdm import tqdm

from rapidocr import EngineType, LangRec, ModelType, OCRVersion, RapidOCR

engine_config = {
    "EngineConfig.tensorrt.use_fp16": True,
    "Rec.engine_type": EngineType.TENSORRT,
    "Rec.lang_type": LangRec.CH,
    "Rec.model_type": ModelType.MOBILE,
    "Rec.ocr_version": OCRVersion.PPOCRV4,
    "Rec.model_path": "rapidocr/models/ch_PP-OCRv4_rec_infer.onnx",
}
engine = RapidOCR(params=engine_config)

dataset = load_dataset("SWHL/text_rec_test_dataset")
test_data = dataset["test"]

content = []
for i, one_data in enumerate(tqdm(test_data)):
    img = np.array(one_data.get("image"))
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    t0 = time.perf_counter()
    result = engine(img, use_rec=True, use_cls=False, use_det=False)
    elapse = time.perf_counter() - t0

    rec_text = result.txts[0]
    if len(rec_text) <= 0:
        rec_text = ""
        elapse = 0

    gt = one_data.get("label", None)
    content.append(f"{rec_text}\t{gt}\t{elapse}")

with open("pred.txt", "w", encoding="utf-8") as f:
    for v in content:
        f.write(f"{v}\n")

# Step 2: 计算指标
from text_rec_metric import TextRecMetric

metric = TextRecMetric()
pred_path = "pred.txt"
metric = metric(pred_path)
print(metric)

# Step 1: 获得推理结果
import time

import cv2
import numpy as np
from datasets import load_dataset
from tqdm import tqdm

from rapidocr import EngineType, LangRec, ModelType, OCRVersion, RapidOCR

engine_config = {
    "EngineConfig.tensorrt.use_fp16": False,  # 默认为FP32
    "Rec.engine_type": EngineType.TENSORRT,
    "Rec.lang_type": LangRec.CH,
    "Rec.model_type": ModelType.MOBILE,
    "Rec.ocr_version": OCRVersion.PPOCRV4,
    "Rec.model_path": "rapidocr/models/ch_PP-OCRv4_rec_infer.onnx",
}
engine = RapidOCR(params=engine_config)

dataset = load_dataset("SWHL/text_rec_test_dataset")
test_data = dataset["test"]

content = []
for i, one_data in enumerate(tqdm(test_data)):
    img = np.array(one_data.get("image"))
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    t0 = time.perf_counter()
    result = engine(img, use_rec=True, use_cls=False, use_det=False)
    elapse = time.perf_counter() - t0

    rec_text = result.txts[0]
    if len(rec_text) <= 0:
        rec_text = ""
        elapse = 0

    gt = one_data.get("label", None)
    content.append(f"{rec_text}\t{gt}\t{elapse}")

with open("pred.txt", "w", encoding="utf-8") as f:
    for v in content:
        f.write(f"{v}\n")

# Step 2: 计算指标
from text_rec_metric import TextRecMetric

metric = TextRecMetric()
pred_path = "pred.txt"
metric = metric(pred_path)
print(metric)

结果对比¶

Tip

1. 仅测试了中文相关的识别模型，其他语言的模型，因为没有对应评测集，就不测试指标了。
2. 下面指标仅作为转换前后，比较模型精度差异使用哈！
3. TensorRT 下的耗时是GPU的，ONNX Runtime 是 CPU 的。

Exp	模型	推理框架	推理引擎	硬件	ExactMatch↑	CharMatch↑	Elapse↓
1	ch_PP-OCRv4_rec_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.8290	0.9432	0.1243
2	ch_PP-OCRv4_rec_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.8290	0.9432	0.0022
3	ch_PP-OCRv4_rec_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.8290	0.9430	0.0023

4	ch_PP-OCRv4_rec_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.8290	0.9432	0.0081
5	ch_PP-OCRv4_rec_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.8290	0.9432	0.0019
6	ch_PP-OCRv4_rec_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.8290	0.9428	0.0015

7	ch_PP-OCRv4_rec_server_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.8065	0.9375	3.3677
8	ch_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.8065	0.9376	0.0047
9	ch_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.8097	0.9376	0.0025

10	ch_PP-OCRv4_rec_server_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.8065	0.9375	4.9997
11	ch_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.8065	0.9375	0.0063
12	ch_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.8097	0.9376	0.0028

13	ch_doc_PP-OCRv4_rec_server_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.8097	0.9444	3.3989
14	ch_doc_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.8097	0.9444	0.0051
15	ch_doc_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.8097	0.9444	0.0028

16	ch_doc_PP-OCRv4_rec_server_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.8097	0.9444	5.2886
17	ch_doc_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.8097	0.9444	0.0062
18	ch_doc_PP-OCRv4_rec_server_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.8065	0.9439	0.0032

19	ch_PP-OCRv5_rec_mobile_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.7355	0.9177	0.1035
20	ch_PP-OCRv5_rec_mobile_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.7355	0.9177	0.0039
21	ch_PP-OCRv5_rec_mobile_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.7387	0.9193	0.0024

22	ch_PP-OCRv5_rec_mobile_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.7355	0.9177	0.0064
23	ch_PP-OCRv5_rec_mobile_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.7355	0.9178	0.0035
24	ch_PP-OCRv5_rec_mobile_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.7387	0.9191	0.0026

25	ch_PP-OCRv5_rec_server_infer	RapidOCR	ONNX Runtime	NVIDIA A800-SXM4-80GB	0.8129	0.9431	1.4705
26	ch_PP-OCRv5_rec_server_infer	RapidOCR	TensorRT FP32	NVIDIA A800-SXM4-80GB	0.8161	0.9439	0.0049
27	ch_PP-OCRv5_rec_server_infer	RapidOCR	TensorRT FP16	NVIDIA A800-SXM4-80GB	0.8129	0.9431	0.0039

28	ch_PP-OCRv5_rec_server_infer	RapidOCR	ONNX Runtime	NVIDIA GeForce RTX 3060	0.8129	0.9431	1.2137
29	ch_PP-OCRv5_rec_server_infer	RapidOCR	TensorRT FP32	NVIDIA GeForce RTX 3060	0.8129	0.9431	0.0051
30	ch_PP-OCRv5_rec_server_infer	RapidOCR	TensorRT FP16	NVIDIA GeForce RTX 3060	0.8129	0.9431	0.0042

写在最后¶

从以上基准比较来看，ONNX 模型在转换为 TensorRT 对应的 .engine 模型后， FP32 精度下，检测和识别模型均在误差范围内，推理速度有量级的提升。如果追求极致的推理速度，欢迎试用。

值得一提的是，我这里仅测试了小批量的数据下效果，难免存在疏漏。更多全面测试，仍需要使用到的小伙伴多多反馈。

rapidocr将在 >=v3.7.0 集成，欢迎届时使用和反馈。

RapidOCR支持TensorRT推理引擎

引言¶

运行环境¶¶

支持Det模型¶

比较转化前后推理精度差异¶

结果对比¶

支持Rec模型¶

比较转化前后推理精度差异¶

结果对比¶

写在最后¶

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