Fix typos (#14936)

Author: co63oc

benchmark/PaddleOCR_DBNet/README.MD

@@ -64,7 +64,7 @@ x1, y1, x2, y2, x3, y3, x4, y4, annotation
 1. config the `dataset['train']['dataset'['data_path']'`,`dataset['validate']['dataset'['data_path']`in [config/icdar2015_resnet18_fpn_DBhead_polyLR.yaml](cconfig/icdar2015_resnet18_fpn_DBhead_polyLR.yaml)
 * . single gpu train
 ```bash
-bash singlel_gpu_train.sh
+bash single_gpu_train.sh
 ```
 * . Multi-gpu training
 ```bash

benchmark/PaddleOCR_DBNet/singlel_gpu_train.sh benchmark/PaddleOCR_DBNet/single_gpu_train.sh

@@ -4,5 +4,5 @@
 from .util import *
 from .metrics import *
 from .schedulers import *
-from .cal_recall.script import cal_recall_precison_f1
+from .cal_recall.script import cal_recall_precision_f1
 from .ocr_metric import get_metric

benchmark/PaddleOCR_DBNet/utils/__init__.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 # @Time    : 1/16/19 6:40 AM
 # @Author  : zhoujun
-from .script import cal_recall_precison_f1
+from .script import cal_recall_precision_f1
 
-__all__ = ["cal_recall_precison_f1"]
+__all__ = ["cal_recall_precision_f1"]

benchmark/PaddleOCR_DBNet/utils/cal_recall/__init__.py

@@ -394,7 +394,7 @@ def compute_ap(confList, matchList, numGtCare):
     return resDict
 
 
-def cal_recall_precison_f1(gt_path, result_path, show_result=False):
+def cal_recall_precision_f1(gt_path, result_path, show_result=False):
     p = {"g": gt_path, "s": result_path}
     result = rrc_evaluation_funcs.main_evaluation(
         p, default_evaluation_params, validate_data, evaluate_method, show_result

benchmark/PaddleOCR_DBNet/utils/cal_recall/script.py

@@ -66,7 +66,7 @@ app主页中有四个按钮，一个下拉列表和一个菜单按钮，他们
 
 <img src="https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/imgs/run_det_cls_rec.jpg" width="400">
 
-模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model successed`表明模型运行成功。
+模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model succeeded`表明模型运行成功。
 
 模型的运行结果显示在运行结果显示区，显示格式为
 ```text

deploy/android_demo/README.md

@@ -256,7 +256,7 @@ public void onLoadModelSuccessed() {
         // Load test image from path and run model
         tvInputSetting.setText("Model: " + modelPath.substring(modelPath.lastIndexOf("/") + 1) + "\nOPENCL: " + cbOpencl.isChecked() + "\nCPU Thread Num: " + cpuThreadNum + "\nCPU Power Mode: " + cpuPowerMode);
         tvInputSetting.scrollTo(0, 0);
-        tvStatus.setText("STATUS: load model successed");
+        tvStatus.setText("STATUS: load model succeeded");
 
     }
 
@@ -265,7 +265,7 @@ public void onLoadModelFailed() {
     }
 
     public void onRunModelSuccessed() {
-        tvStatus.setText("STATUS: run model successed");
+        tvStatus.setText("STATUS: run model succeeded");
         // Obtain results and update UI
         tvInferenceTime.setText("Inference time: " + predictor.inferenceTime() + " ms");
         Bitmap outputImage = predictor.outputImage();

deploy/android_demo/app/src/main/java/com/baidu/paddle/lite/demo/ocr/MainActivity.java

@@ -314,7 +314,7 @@ class MakeShrinkMap(object):
                 padding = pyclipper.PyclipperOffset()
                 padding.AddPath(subject, pyclipper.JT_ROUND,
                                 pyclipper.ET_CLOSEDPOLYGON)
-                shrinked = []
+                shrunk = []
 
                 # Increase the shrink ratio every time we get multiple polygon returned back
                 possible_ratios = np.arange(self.shrink_ratio, 1,
@@ -323,19 +323,19 @@ class MakeShrinkMap(object):
                 for ratio in possible_ratios:
                     distance = polygon_shape.area * (
                         1 - np.power(ratio, 2)) / polygon_shape.length
-                    shrinked = padding.Execute(-distance)
-                    if len(shrinked) == 1:
+                    shrunk = padding.Execute(-distance)
+                    if len(shrunk) == 1:
                         break
 
-                if shrinked == []:
+                if shrunk == []:
                     cv2.fillPoly(mask,
                                  polygon.astype(np.int32)[np.newaxis, :, :], 0)
                     ignore_tags[i] = True
                     continue
 
-                for each_shirnk in shrinked:
-                    shirnk = np.array(each_shirnk).reshape(-1, 2)
-                    cv2.fillPoly(gt, [shirnk.astype(np.int32)], 1)
+                for each_shrink in shrunk:
+                    shrink = np.array(each_shrink).reshape(-1, 2)
+                    cv2.fillPoly(gt, [shrink.astype(np.int32)], 1)
                     if self.num_classes > 1:  # 绘制分类的mask
                         cv2.fillPoly(gt_class, polygon.astype(np.int32)[np.newaxis, :, :], classes[i])
 

docs/applications/快速构建卡证类OCR.md

@@ -64,7 +64,7 @@ When you tap **Run Model**, the demo executes the corresponding model(s) in your
 
 <img src="./images/run_det_cls_rec.jpg" width="400">
 
-The status display area shows the current model status (e.g., `run model successed`), indicating that the model ran successfully. The recognition results are formatted as follows:
+The status display area shows the current model status (e.g., `run model succeeded`), indicating that the model ran successfully. The recognition results are formatted as follows:
 
 ```text
 Serial Number: Det: (x1,y1)(x2,y2)(x3,y3)(x4,y4) Rec: Recognized Text, Confidence Score Cls: Classification Label, Classification Score

docs/infer_deploy/android_demo.en.md

@@ -63,7 +63,7 @@ app主页中有四个按钮，一个下拉列表和一个菜单按钮，他们
 
 <img src="./images/run_det_cls_rec.jpg" width="400">
 
-模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model successed`表明模型运行成功。
+模型运行完成后，模型和运行状态显示区`STATUS`字段显示了当前模型的运行状态，这里显示为`run model succeeded`表明模型运行成功。
 
 模型的运行结果显示在运行结果显示区，显示格式为
 

docs/infer_deploy/android_demo.md

@@ -2,7 +2,7 @@
 
 The All-in-One development tool [PaddleX](https://github.com/PaddlePaddle/PaddleX/tree/release/3.0-beta1), based on the advanced technology of PaddleOCR, supports **low-code full-process** development capabilities in the OCR field. Through low-code development, simple and efficient model use, combination, and customization can be achieved. This will significantly **reduce the time consumption** of model development, **lower its development difficulty**, and greatly accelerate the application and promotion speed of models in the industry. Features include:
 
-* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **48 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 10 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forcasting, forming 30+ single-function modules, making it convenient for developers to use **model combinations**.
+* 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **48 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 10 pipelines, which can be quickly experienced through a simple **Python API one-click call**. In addition, the same set of APIs also supports a total of **200+ models** in image classification, object detection, image segmentation, and time series forecasting, forming 30+ single-function modules, making it convenient for developers to use **model combinations**.
 
 * 🚀 [**High Efficiency and Low barrier of entry**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/overview.html): Provides two methods based on **unified commands** and **GUI** to achieve simple and efficient use, combination, and customization of models. Supports multiple deployment methods such as **high-performance inference, service-oriented deployment, and edge deployment**. Additionally, for various mainstream hardware such as **NVIDIA GPU, Kunlunxin XPU, Ascend NPU, Cambricon MLU, and Haiguang DCU**, models can be developed with **seamless switching**.