Fix (#14795)

2025-03-04 09:38:37 +08:00 · 2025-03-04 09:38:37 +08:00 · f35dfef921
parent 1d52a2b2e1
commit f35dfef921
37 changed files with 65 additions and 64 deletions
--- a/benchmark/PaddleOCR_DBNet/models/backbone/resnet.py
+++ b/benchmark/PaddleOCR_DBNet/models/backbone/resnet.py
@ -265,7 +265,7 @@ def resnet18(pretrained=True, **kwargs):
    if pretrained:
        assert (
            kwargs.get("in_channels", 3) == 3
-        ), "in_channels must be 3 whem pretrained is True"
+        ), "in_channels must be 3 when pretrained is True"
        print("load from imagenet")
        load_models(model, "resnet18")
    return model
@ -280,7 +280,7 @@ def deformable_resnet18(pretrained=True, **kwargs):
    if pretrained:
        assert (
            kwargs.get("in_channels", 3) == 3
-        ), "in_channels must be 3 whem pretrained is True"
+        ), "in_channels must be 3 when pretrained is True"
        print("load from imagenet")
        model.load_state_dict(model_zoo.load_url(model_urls["resnet18"]), strict=False)
    return model
@ -295,7 +295,7 @@ def resnet34(pretrained=True, **kwargs):
    if pretrained:
        assert (
            kwargs.get("in_channels", 3) == 3
-        ), "in_channels must be 3 whem pretrained is True"
+        ), "in_channels must be 3 when pretrained is True"
        model.load_state_dict(model_zoo.load_url(model_urls["resnet34"]), strict=False)
    return model

@ -309,7 +309,7 @@ def resnet50(pretrained=True, **kwargs):
    if pretrained:
        assert (
            kwargs.get("in_channels", 3) == 3
-        ), "in_channels must be 3 whem pretrained is True"
+        ), "in_channels must be 3 when pretrained is True"
        load_models(model, "resnet50")
    return model

@ -323,7 +323,7 @@ def deformable_resnet50(pretrained=True, **kwargs):
    if pretrained:
        assert (
            kwargs.get("in_channels", 3) == 3
-        ), "in_channels must be 3 whem pretrained is True"
+        ), "in_channels must be 3 when pretrained is True"
        model.load_state_dict(model_zoo.load_url(model_urls["resnet50"]), strict=False)
    return model

@ -337,7 +337,7 @@ def resnet101(pretrained=True, **kwargs):
    if pretrained:
        assert (
            kwargs.get("in_channels", 3) == 3
-        ), "in_channels must be 3 whem pretrained is True"
+        ), "in_channels must be 3 when pretrained is True"
        model.load_state_dict(model_zoo.load_url(model_urls["resnet101"]), strict=False)
    return model

@ -351,7 +351,7 @@ def resnet152(pretrained=True, **kwargs):
    if pretrained:
        assert (
            kwargs.get("in_channels", 3) == 3
-        ), "in_channels must be 3 whem pretrained is True"
+        ), "in_channels must be 3 when pretrained is True"
        model.load_state_dict(model_zoo.load_url(model_urls["resnet152"]), strict=False)
    return model

--- a/benchmark/PaddleOCR_DBNet/models/losses/basic_loss.py
+++ b/benchmark/PaddleOCR_DBNet/models/losses/basic_loss.py
@ -58,7 +58,7 @@ class DiceLoss(nn.Layer):
    """
    Loss function from https://arxiv.org/abs/1707.03237,
    where iou computation is introduced heatmap manner to measure the
-    diversity bwtween tow heatmaps.
+    diversity between tow heatmaps.
    """

    def __init__(self, eps=1e-6):
--- a/benchmark/PaddleOCR_DBNet/post_processing/seg_detector_representer.py
+++ b/benchmark/PaddleOCR_DBNet/post_processing/seg_detector_representer.py
@ -27,7 +27,7 @@ class SegDetectorRepresenter:
        pred:
            binary: text region segmentation map, with shape (N, H, W)
            thresh: [if exists] thresh hold prediction with shape (N, H, W)
-            thresh_binary: [if exists] binarized with threshhold, (N, H, W)
+            thresh_binary: [if exists] binarized with threshold, (N, H, W)
        """
        if isinstance(pred, paddle.Tensor):
            pred = pred.numpy()
--- a/benchmark/PaddleOCR_DBNet/tools/infer.py
+++ b/benchmark/PaddleOCR_DBNet/tools/infer.py
@ -96,7 +96,7 @@ class InferenceEngine(object):
        initialize the inference engine
        Args:
            model_file_path: inference model path (*.pdmodel)
-            model_file_path: inference parmaeter path (*.pdiparams)
+            model_file_path: inference parameter path (*.pdiparams)
        Return:
            predictor: Predictor created using Paddle Inference.
            config: Configuration of the predictor.
@ -112,7 +112,7 @@ class InferenceEngine(object):
                    workspace_size=1 << 30,
                    precision_mode=precision,
                    max_batch_size=args.max_batch_size,
-                    min_subgraph_size=args.min_subgraph_size,  # skip the minmum trt subgraph
+                    min_subgraph_size=args.min_subgraph_size,  # skip the minimum trt subgraph
                    use_calib_mode=False,
                )

--- a/benchmark/PaddleOCR_DBNet/utils/cal_recall/rrc_evaluation_funcs.py
+++ b/benchmark/PaddleOCR_DBNet/utils/cal_recall/rrc_evaluation_funcs.py
@ -25,7 +25,7 @@ def print_help():
 def load_zip_file_keys(file, fileNameRegExp=""):
    """
    Returns an array with the entries of the ZIP file that match with the regular expression.
-    The key's are the names or the file or the capturing group definied in the fileNameRegExp
+    The key's are the names or the file or the capturing group defined in the fileNameRegExp
    """
    try:
        archive = zipfile.ZipFile(file, mode="r", allowZip64=True)
@ -54,7 +54,7 @@ def load_zip_file_keys(file, fileNameRegExp=""):
 def load_zip_file(file, fileNameRegExp="", allEntries=False):
    """
    Returns an array with the contents (filtered by fileNameRegExp) of a ZIP file.
-    The key's are the names or the file or the capturing group definied in the fileNameRegExp
+    The key's are the names or the file or the capturing group defined in the fileNameRegExp
    allEntries validates that all entries in the ZIP file pass the fileNameRegExp
    """
    try:
@ -86,7 +86,7 @@ def load_zip_file(file, fileNameRegExp="", allEntries=False):
 def load_folder_file(file, fileNameRegExp="", allEntries=False):
    """
    Returns an array with the contents (filtered by fileNameRegExp) of a ZIP file.
-    The key's are the names or the file or the capturing group definied in the fileNameRegExp
+    The key's are the names or the file or the capturing group defined in the fileNameRegExp
    allEntries validates that all entries in the ZIP file pass the fileNameRegExp
    """
    pairs = []
@ -164,8 +164,8 @@ def validate_tl_line(
 ):
    """
    Validate the format of the line. If the line is not valid an exception will be raised.
-    If maxWidth and maxHeight are specified, all points must be inside the imgage bounds.
-    Posible values are:
+    If maxWidth and maxHeight are specified, all points must be inside the image bounds.
+    Possible values are:
    LTRB=True: xmin,ymin,xmax,ymax[,confidence][,transcription]
    LTRB=False: x1,y1,x2,y2,x3,y3,x4,y4[,confidence][,transcription]
    """
@ -182,8 +182,8 @@ def get_tl_line_values(
 ):
    """
    Validate the format of the line. If the line is not valid an exception will be raised.
-    If maxWidth and maxHeight are specified, all points must be inside the imgage bounds.
-    Posible values are:
+    If maxWidth and maxHeight are specified, all points must be inside the image bounds.
+    Possible values are:
    LTRB=True: xmin,ymin,xmax,ymax[,confidence][,transcription]
    LTRB=False: x1,y1,x2,y2,x3,y3,x4,y4[,confidence][,transcription]
    Returns values from a textline. Points , [Confidences], [Transcriptions]
@ -409,11 +409,11 @@ def main_evaluation(
    per_sample=True,
 ):
    """
-    This process validates a method, evaluates it and if it succed generates a ZIP file with a JSON entry for each sample.
+    This process validates a method, evaluates it and if it succeed generates a ZIP file with a JSON entry for each sample.
    Params:
-    p: Dictionary of parmeters with the GT/submission locations. If None is passed, the parameters send by the system are used.
+    p: Dictionary of parameters with the GT/submission locations. If None is passed, the parameters send by the system are used.
    default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation
-    validate_data_fn: points to a method that validates the corrct format of the submission
+    validate_data_fn: points to a method that validates the correct format of the submission
    evaluate_method_fn: points to a function that evaluated the submission and return a Dictionary with the results
    """
    evalParams = default_evaluation_params_fn()
@ -476,7 +476,7 @@ def main_validation(default_evaluation_params_fn, validate_data_fn):
    This process validates a method
    Params:
    default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation
-    validate_data_fn: points to a method that validates the corrct format of the submission
+    validate_data_fn: points to a method that validates the correct format of the submission
    """
    try:
        p = dict([s[1:].split("=") for s in sys.argv[1:]])
--- a/deploy/README.md
+++ b/deploy/README.md
@ -2,8 +2,9 @@ English | [简体中文](README_ch.md)

 # PP-OCR Deployment

- [Paddle Deployment Introduction](#1)
- [PP-OCR Deployment](#2)
+- [PP-OCR Deployment](#pp-ocr-deployment)
+  - [Paddle Deployment Introduction](#paddle-deployment-introduction)
+  - [PP-OCR Deployment](#pp-ocr-deployment-1)

 <a name="1"></a>
 ## Paddle Deployment Introduction
@ -18,7 +19,7 @@ Paddle provides a variety of deployment schemes to meet the deployment requireme
 <a name="2"></a>
 ## PP-OCR Deployment

-PP-OCR has supported muti deployment schemes. Click the link to get the specific tutorial.
+PP-OCR has supported multi deployment schemes. Click the link to get the specific tutorial.

 - [Python Inference](../doc/doc_en/inference_ppocr_en.md)
 - [C++ Inference](./cpp_infer/readme.md)
--- a/deploy/android_demo/README.md
+++ b/deploy/android_demo/README.md
@ -28,7 +28,7 @@
 ## 3. 快速使用

 ### 3.1 环境准备
-1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
+1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Studio 官网](https://developer.android.com/studio)。
 2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`

 **注意**：如果您的 Android Studio 尚未配置 NDK ，请根据 Android Studio 用户指南中的[安装及配置 NDK 和 CMake ](https://developer.android.com/studio/projects/install-ndk)内容，预先配置好 NDK 。您可以选择最新的 NDK 版本，或者使用 Paddle Lite 预测库版本一样的 NDK
--- a/deploy/android_demo/app/src/main/cpp/ocr_clipper.cpp
+++ b/deploy/android_demo/app/src/main/cpp/ocr_clipper.cpp
@ -2566,7 +2566,7 @@ void Clipper::ProcessHorizontal(TEdge *horzEdge) {
    while (e) {

      // this code block inserts extra coords into horizontal edges (in output
-      // polygons) whereever maxima touch these horizontal edges. This helps
+      // polygons) wherever maxima touch these horizontal edges. This helps
      //'simplifying' polygons (ie if the Simplify property is set).
      if (m_Maxima.size() > 0) {
        if (dir == dLeftToRight) {
--- a/deploy/cpp_infer/include/clipper.h
+++ b/deploy/cpp_infer/include/clipper.h
@ -38,7 +38,7 @@
 // improve performance but coordinate values are limited to the range +/- 46340
 //#define use_int32

-// use_xyz: adds a Z member to IntPoint. Adds a minor cost to perfomance.
+// use_xyz: adds a Z member to IntPoint. Adds a minor cost to performance.
 //#define use_xyz

 // use_lines: Enables line clipping. Adds a very minor cost to performance.
--- a/deploy/cpp_infer/src/args.cpp
+++ b/deploy/cpp_infer/src/args.cpp
@ -59,7 +59,7 @@ DEFINE_string(layout_dict_path,
 DEFINE_double(layout_score_threshold, 0.5, "Threshold of score.");
 DEFINE_double(layout_nms_threshold, 0.5, "Threshold of nms.");
 // structure model related
-DEFINE_string(table_model_dir, "", "Path of table struture inference model.");
+DEFINE_string(table_model_dir, "", "Path of table structure inference model.");
 DEFINE_int32(table_max_len, 488, "max len size of input image.");
 DEFINE_int32(table_batch_num, 1, "table_batch_num.");
 DEFINE_bool(merge_no_span_structure, true,
--- a/deploy/cpp_infer/src/clipper.cpp
+++ b/deploy/cpp_infer/src/clipper.cpp
@ -2571,7 +2571,7 @@ void Clipper::ProcessHorizontal(TEdge *horzEdge) noexcept {
    while (e) {

      // this code block inserts extra coords into horizontal edges (in output
-      // polygons) whereever maxima touch these horizontal edges. This helps
+      // polygons) wherever maxima touch these horizontal edges. This helps
      //'simplifying' polygons (ie if the Simplify property is set).
      if (m_Maxima.size() > 0) {
        if (dir == dLeftToRight) {
--- a/deploy/cpp_infer/src/main.cpp
+++ b/deploy/cpp_infer/src/main.cpp
@ -74,7 +74,7 @@ void check_params() {
  }
  if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" &&
      FLAGS_precision != "int8") {
-    std::cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. "
+    std::cout << "precision should be 'fp32'(default), 'fp16' or 'int8'. "
              << std::endl;
    exit(1);
  }
--- a/deploy/fastdeploy/android/README.md
+++ b/deploy/fastdeploy/android/README.md
@ -5,7 +5,7 @@

 ## 环境准备

-1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
+1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Studio 官网](https://developer.android.com/studio)。
 2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`

 ## 部署步骤
--- a/deploy/fastdeploy/cpu-gpu/python/infer.py
+++ b/deploy/fastdeploy/cpu-gpu/python/infer.py
@ -217,7 +217,7 @@ ppocr_v3.rec_batch_size = args.rec_bs
 # Read the input image
 im = cv2.imread(args.image)

-# Predict and reutrn the results
+# Predict and return the results
 result = ppocr_v3.predict(im)

 print(result)
--- a/deploy/fastdeploy/serving/fastdeploy_serving/models/cls_postprocess/1/model.py
+++ b/deploy/fastdeploy/serving/fastdeploy_serving/models/cls_postprocess/1/model.py
@ -33,7 +33,7 @@ class TritonPythonModel:
    def initialize(self, args):
        """`initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
-        the model to intialize any state associated with this model.
+        the model to initialize any state associated with this model.
        Parameters
        ----------
        args : dict
--- a/deploy/fastdeploy/serving/fastdeploy_serving/models/det_postprocess/1/model.py
+++ b/deploy/fastdeploy/serving/fastdeploy_serving/models/det_postprocess/1/model.py
@ -83,7 +83,7 @@ class TritonPythonModel:
    def initialize(self, args):
        """`initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
-        the model to intialize any state associated with this model.
+        the model to initialize any state associated with this model.
        Parameters
        ----------
        args : dict
--- a/deploy/fastdeploy/serving/fastdeploy_serving/models/det_preprocess/1/model.py
+++ b/deploy/fastdeploy/serving/fastdeploy_serving/models/det_preprocess/1/model.py
@ -33,7 +33,7 @@ class TritonPythonModel:
    def initialize(self, args):
        """`initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
-        the model to intialize any state associated with this model.
+        the model to initialize any state associated with this model.
        Parameters
        ----------
        args : dict
--- a/deploy/fastdeploy/serving/fastdeploy_serving/models/rec_postprocess/1/model.py
+++ b/deploy/fastdeploy/serving/fastdeploy_serving/models/rec_postprocess/1/model.py
@ -35,7 +35,7 @@ class TritonPythonModel:
    def initialize(self, args):
        """`initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
-        the model to intialize any state associated with this model.
+        the model to initialize any state associated with this model.
        Parameters
        ----------
        args : dict
--- a/deploy/hubserving/kie_ser/module.py
+++ b/deploy/hubserving/kie_ser/module.py
@ -72,7 +72,7 @@ class KIESer(hub.Module):
    def merge_configs(
        self,
    ):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/kie_ser_re/module.py
+++ b/deploy/hubserving/kie_ser_re/module.py
@ -72,7 +72,7 @@ class KIESerRE(hub.Module):
    def merge_configs(
        self,
    ):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/ocr_cls/module.py
+++ b/deploy/hubserving/ocr_cls/module.py
@ -68,7 +68,7 @@ class OCRCls(hub.Module):
    def merge_configs(
        self,
    ):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/ocr_det/module.py
+++ b/deploy/hubserving/ocr_det/module.py
@ -70,7 +70,7 @@ class OCRDet(hub.Module):
    def merge_configs(
        self,
    ):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/ocr_det/params.py
+++ b/deploy/hubserving/ocr_det/params.py
@ -30,14 +30,14 @@ def read_params():
    cfg.det_limit_side_len = 960
    cfg.det_limit_type = "max"

-    # DB parmas
+    # DB params
    cfg.det_db_thresh = 0.3
    cfg.det_db_box_thresh = 0.6
    cfg.det_db_unclip_ratio = 1.5
    cfg.use_dilation = False
    cfg.det_db_score_mode = "fast"

-    # #EAST parmas
+    # #EAST params
    # cfg.det_east_score_thresh = 0.8
    # cfg.det_east_cover_thresh = 0.1
    # cfg.det_east_nms_thresh = 0.2
--- a/deploy/hubserving/ocr_rec/module.py
+++ b/deploy/hubserving/ocr_rec/module.py
@ -68,7 +68,7 @@ class OCRRec(hub.Module):
    def merge_configs(
        self,
    ):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/ocr_system/module.py
+++ b/deploy/hubserving/ocr_system/module.py
@ -71,7 +71,7 @@ class OCRSystem(hub.Module):
    def merge_configs(
        self,
    ):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/ocr_system/params.py
+++ b/deploy/hubserving/ocr_system/params.py
@ -30,14 +30,14 @@ def read_params():
    cfg.det_limit_side_len = 960
    cfg.det_limit_type = "max"

-    # DB parmas
+    # DB params
    cfg.det_db_thresh = 0.3
    cfg.det_db_box_thresh = 0.5
    cfg.det_db_unclip_ratio = 1.6
    cfg.use_dilation = False
    cfg.det_db_score_mode = "fast"

-    # EAST parmas
+    # EAST params
    cfg.det_east_score_thresh = 0.8
    cfg.det_east_cover_thresh = 0.1
    cfg.det_east_nms_thresh = 0.2
--- a/deploy/hubserving/structure_layout/module.py
+++ b/deploy/hubserving/structure_layout/module.py
@ -67,7 +67,7 @@ class LayoutPredictor(hub.Module):
        self.layout_predictor = _LayoutPredictor(cfg)

    def merge_configs(self):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/structure_system/module.py
+++ b/deploy/hubserving/structure_system/module.py
@ -70,7 +70,7 @@ class StructureSystem(hub.Module):
        self.table_sys = PPStructureSystem(cfg)

    def merge_configs(self):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/hubserving/structure_table/module.py
+++ b/deploy/hubserving/structure_table/module.py
@ -69,7 +69,7 @@ class TableSystem(hub.Module):
        self.table_sys = _TableSystem(cfg)

    def merge_configs(self):
-        # deafult cfg
+        # default cfg
        backup_argv = copy.deepcopy(sys.argv)
        sys.argv = sys.argv[:1]
        cfg = parse_args()
--- a/deploy/paddlejs/README.md
+++ b/deploy/paddlejs/README.md
@ -13,7 +13,7 @@ Run OCR demo in browser refer to [tutorial](https://github.com/PaddlePaddle/Fast


 ## Mini Program Demo
-The Mini Program demo running tutorial eference
+The Mini Program demo running tutorial reference
 Run OCR demo in wechat miniprogram refer to [tutorial](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/application/js/mini_program).

 |demo|directory|
--- a/docs/algorithm/text_recognition/algorithm_rec_cppd.en.md
+++ b/docs/algorithm/text_recognition/algorithm_rec_cppd.en.md
@ -40,7 +40,7 @@ The accuracy (%) and model files of CPPD on the public dataset of scene text rec
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
 | CPPD Base 32*128  | 87.5  | 70.7 |   78.2   | 82.9  | 85.5 | 85.4 | 84.3 | 82.08 | Same as the table above. |

-* Chinese dataset from [Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition).
+* Chinese dataset from [Chinese Benchmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition).

 |    Model      | Scene | Web | Document | Handwriting | Avg |      Download       |
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|
--- a/docs/algorithm/text_recognition/algorithm_rec_cppd.md
+++ b/docs/algorithm/text_recognition/algorithm_rec_cppd.md
@ -52,7 +52,7 @@ CPPD在场景文本识别公开数据集上的精度(%)和模型文件如下：
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|
 | CPPD Base 32*128  | 87.5  | 70.7 |   78.2   | 82.9  | 85.5 | 85.4 | 84.3 | 82.08 | 同上表 |

-* 中文训练集和测试集来自于[Chinese Benckmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition)。
+* 中文训练集和测试集来自于[Chinese Benchmark](https://github.com/FudanVI/benchmarking-chinese-text-recognition)。

 |    模型      | Scene | Web | Document | Handwriting | Avg |      下载链接       |
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|
--- a/docs/algorithm/text_recognition/algorithm_rec_svtr.en.md
+++ b/docs/algorithm/text_recognition/algorithm_rec_svtr.en.md
@ -13,7 +13,7 @@ Paper:

 The accuracy (%) and model files of SVTR on the public dataset of scene text recognition are as follows:

-* Chinese dataset from [Chinese Benckmark](https://arxiv.org/abs/2112.15093) , and the Chinese training evaluation strategy of SVTR follows the paper.
+* Chinese dataset from [Chinese Benchmark](https://arxiv.org/abs/2112.15093) , and the Chinese training evaluation strategy of SVTR follows the paper.

 |   Model    |IC13<br/>857 |  SVT  |IIIT5k<br/>3000 |IC15<br/>1811| SVTP  |CUTE80 | Avg_6 |IC15<br/>2077 |IC13<br/>1015 |IC03<br/>867|IC03<br/>860|Avg_10 | Chinese<br/>scene_test|                                                                                            Download link                                                                                            |
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|:-----:|:-----:|:---------------------------------------------:|:-----:|:------:|
--- a/docs/algorithm/text_recognition/algorithm_rec_svtr.md
+++ b/docs/algorithm/text_recognition/algorithm_rec_svtr.md
@ -23,7 +23,7 @@ comments: true

 SVTR在场景文本识别公开数据集上的精度(%)和模型文件如下：

-* 中文数据集来自于[Chinese Benckmark](https://arxiv.org/abs/2112.15093) ，SVTR的中文训练评估策略遵循该论文。
+* 中文数据集来自于[Chinese Benchmark](https://arxiv.org/abs/2112.15093) ，SVTR的中文训练评估策略遵循该论文。

 |    模型      |IC13<br/>857 |  SVT  |IIIT5k<br/>3000 |IC15<br/>1811| SVTP  |CUTE80 | Avg_6 |IC15<br/>2077 |IC13<br/>1015 |IC03<br/>867|IC03<br/>860|Avg_10 | Chinese<br/>scene_test|      下载链接       |
 |:----------:|:------:|:-----:|:---------:|:------:|:-----:|:-----:|:-----:|:-------:|:-------:|:-----:|:-----:|:------:|:-----:|:-----:|
--- a/docs/infer_deploy/android_demo.md
+++ b/docs/infer_deploy/android_demo.md
@ -27,7 +27,7 @@ hide:

 ### 3.1 环境准备

-1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Stuido 官网](https://developer.android.com/studio)。
+1. 在本地环境安装好 Android Studio 工具，详细安装方法请见[Android Studio 官网](https://developer.android.com/studio)。
 2. 准备一部 Android 手机，并开启 USB 调试模式。开启方法: `手机设置 -> 查找开发者选项 -> 打开开发者选项和 USB 调试模式`

 **注意**：如果您的 Android Studio 尚未配置 NDK ，请根据 Android Studio 用户指南中的[安装及配置 NDK 和 CMake](https://developer.android.com/studio/projects/install-ndk)内容，预先配置好 NDK 。您可以选择最新的 NDK 版本，或者使用 Paddle Lite 预测库版本一样的 NDK
--- a/ppocr/postprocess/east_postprocess.py
+++ b/ppocr/postprocess/east_postprocess.py
@ -85,7 +85,7 @@ class EASTPostProcess(object):
        if boxes.shape[0] == 0:
            return []
        # Here we filter some low score boxes by the average score map,
-        #   this is different from the orginal paper.
+        #   this is different from the original paper.
        for i, box in enumerate(boxes):
            mask = np.zeros_like(score_map, dtype=np.uint8)
            cv2.fillPoly(mask, box[:8].reshape((-1, 4, 2)).astype(np.int32) // 4, 1)
--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@ -63,7 +63,7 @@ def init_args():
    parser.add_argument("--det_limit_type", type=str, default="max")
    parser.add_argument("--det_box_type", type=str, default="quad")

-    # DB parmas
+    # DB params
    parser.add_argument("--det_db_thresh", type=float, default=0.3)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
@ -71,22 +71,22 @@ def init_args():
    parser.add_argument("--use_dilation", type=str2bool, default=False)
    parser.add_argument("--det_db_score_mode", type=str, default="fast")

-    # EAST parmas
+    # EAST params
    parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
    parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)

-    # SAST parmas
+    # SAST params
    parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
    parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)

-    # PSE parmas
+    # PSE params
    parser.add_argument("--det_pse_thresh", type=float, default=0)
    parser.add_argument("--det_pse_box_thresh", type=float, default=0.85)
    parser.add_argument("--det_pse_min_area", type=float, default=16)
    parser.add_argument("--det_pse_scale", type=int, default=1)

-    # FCE parmas
+    # FCE params
    parser.add_argument("--scales", type=list, default=[8, 16, 32])
    parser.add_argument("--alpha", type=float, default=1.0)
    parser.add_argument("--beta", type=float, default=1.0)
@ -112,7 +112,7 @@ def init_args():
    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
    parser.add_argument("--e2e_limit_type", type=str, default="max")

-    # PGNet parmas
+    # PGNet params
    parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
    parser.add_argument(
        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt"
@ -133,7 +133,7 @@ def init_args():
    parser.add_argument("--use_pdserving", type=str2bool, default=False)
    parser.add_argument("--warmup", type=str2bool, default=False)

-    # SR parmas
+    # SR params
    parser.add_argument("--sr_model_dir", type=str)
    parser.add_argument("--sr_image_shape", type=str, default="3, 32, 128")
    parser.add_argument("--sr_batch_num", type=int, default=1)