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benchmark_en.md 1.90 KB

# Benchmark

This document gives the performance of the series models for Chinese and English recognition.

## Test Data

We collected 300 images for different real application scenarios to evaluate the overall OCR system, including contract samples, license plates, nameplates, train tickets, test sheets, forms, certificates, street view images, business cards, digital meter, etc. The following figure shows some images of the test set.

<div align="center">
<img src="../datasets/doc.jpg"  width = "1000" height = "500" />
</div>

## Measurement

Explanation:

- The long size of the input for the text detector is 960.

- The evaluation time-consuming stage is the complete stage from image input to result output, including image pre-processing and post-processing.

- ```Intel Xeon 6148``` is the server-side CPU model. Intel MKL-DNN is used in the test to accelerate the CPU prediction speed.

- ```Snapdragon 855``` is a mobile processing platform model.

Compares the model size and F-score:

| Model Name                    | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score |
|:-:|:-:|:-:|:-:|:-:|:-:|
| PP-OCRv2                 | 11\.6        | 3\.0        | 0\.9           | 8\.6        | 0\.5224      |
| PP-OCR mobile            |   8\.1       | 2\.6        | 0\.9           | 4\.6        | 0\.503       |
| PP-OCR server            | 155\.1       | 47\.2       | 0\.9           | 107         | 0\.570       |

Compares the time-consuming on CPU and T4 GPU (ms):

| Model Name    | CPU  | T4 GPU |
|:-:|:-:|:-:|
| PP-OCRv2      | 330  | 111 |
| PP-OCR mobile | 356  | 116|
| PP-OCR server | 1056 | 200 |

More indicators of PP-OCR series models can be referred to [PP-OCR Benchmark](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md)

一键复制原始数据按行查看历史

提交于 2021-09-23 20:58 . Merge pull request #4044 from Evezerest/2.3

Benchmark

This document gives the performance of the series models for Chinese and English recognition.

Test Data

We collected 300 images for different real application scenarios to evaluate the overall OCR system, including contract samples, license plates, nameplates, train tickets, test sheets, forms, certificates, street view images, business cards, digital meter, etc. The following figure shows some images of the test set.

Measurement

Explanation:

The long size of the input for the text detector is 960.
The evaluation time-consuming stage is the complete stage from image input to result output, including image pre-processing and post-processing.
Intel Xeon 6148 is the server-side CPU model. Intel MKL-DNN is used in the test to accelerate the CPU prediction speed.
Snapdragon 855 is a mobile processing platform model.

Compares the model size and F-score:

Model Name	Model Size of the Whole System(M)	Model Size of the Text Detector(M)	Model Size of the Direction Classifier(M)	Model Size of the Text Recognizer (M)	F-score
PP-OCRv2	11.6	3.0	0.9	8.6	0.5224
PP-OCR mobile	8.1	2.6	0.9	4.6	0.503
PP-OCR server	155.1	47.2	0.9	107	0.570

Compares the time-consuming on CPU and T4 GPU (ms):

Model Name	CPU	T4 GPU
PP-OCRv2	330	111
PP-OCR mobile	356	116
PP-OCR server	1056	200

More indicators of PP-OCR series models can be referred to PP-OCR Benchmark

基于飞桨的OCR工具库，包含总模型仅8.6M的超轻量级中文OCR，单模型支持中英文数字组合识别、竖排文本识别、长文本识别。同时支持多种文本检测、文本识别的训练算法。

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