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Benchmark
本文给出了中英文OCR系列模型精度指标和在各平台预测耗时的benchmark。
测试数据
针对OCR实际应用场景，包括合同，车牌，铭牌，火车票，化验单，表格，证书，街景文字，名片，数码显示屏等，收集的300张图像，每张图平均有17个文本框，下图给出了一些图像示例。
评估指标
说明：
- 检测输入图像的长边尺寸是960。
- 评估耗时阶段为图像预测耗时，不包括图像的预处理和后处理。
Intel至强6148为服务器端CPU型号，测试中使用Intel MKL-DNN 加速。骁龙855为移动端处理平台型号。
预测模型大小和整体识别精度对比
| 模型名称 | 整体模型 大小(M) |
检测模型 大小(M) |
方向分类器 模型大小(M) |
识别模型 大小(M) |
整体识别 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 |
预测模型在CPU和GPU上的速度对比，单位ms
| 模型名称 | CPU | T4 GPU |
|---|---|---|
| PP-OCRv2 | 330 | 111 |
| PP-OCR mobile | 356 | 11 6 |
| PP-OCR server | 1056 | 200 |