Fix (#14798)

benchmark/PaddleOCR_DBNet/data_loader/__init__.py

@@ -67,9 +67,9 @@ def get_dataloader(module_config, distributed=False):
     config = copy.deepcopy(module_config)
     dataset_args = config["dataset"]["args"]
     if "transforms" in dataset_args:
-        img_transfroms = get_transforms(dataset_args.pop("transforms"))
+        img_transforms = get_transforms(dataset_args.pop("transforms"))
     else:
-        img_transfroms = None
+        img_transforms = None
     # 创建数据集
     dataset_name = config["dataset"]["type"]
     data_path = dataset_args.pop("data_path")
@@ -91,7 +91,7 @@ def get_dataloader(module_config, distributed=False):
     _dataset = get_dataset(
         data_path=data_path,
         module_name=dataset_name,
-        transform=img_transfroms,
+        transform=img_transforms,
         dataset_args=dataset_args,
     )
     sampler = None

deploy/android_demo/app/src/main/cpp/ocr_clipper.cpp

@@ -1570,7 +1570,7 @@ bool Clipper::ExecuteInternal() {
 
 void Clipper::SetWindingCount(TEdge &edge) {
   TEdge *e = edge.PrevInAEL;
-  // find the edge of the same polytype that immediately preceeds 'edge' in AEL
+  // find the edge of the same polytype that immediately precedes 'edge' in AEL
   while (e && ((e->PolyTyp != edge.PolyTyp) || (e->WindDelta == 0)))
     e = e->PrevInAEL;
   if (!e) {

deploy/android_demo/app/src/main/cpp/ocr_clipper.hpp

@@ -36,7 +36,7 @@
 // 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.

deploy/android_demo/app/src/main/cpp/ocr_ppredictor.h

@@ -110,7 +110,7 @@ private:
   ClsPredictResult infer_cls(const cv::Mat &origin, float thresh = 0.9);
 
   /**
-   * Postprocess or sencod model to extract text
+   * Postprocess or second model to extract text
    * @param res
    * @return
    */

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

@@ -53,7 +53,7 @@ public class OCRPredictorNative {
 
     }
 
-    public void destory() {
+    public void destroy() {
         if (nativePointer != 0) {
             release(nativePointer);
             nativePointer = 0;

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

@@ -101,7 +101,7 @@ public class Predictor {
 
     public void releaseModel() {
         if (paddlePredictor != null) {
-            paddlePredictor.destory();
+            paddlePredictor.destroy();
             paddlePredictor = null;
         }
         isLoaded = false;

deploy/cpp_infer/src/clipper.cpp

@@ -1573,7 +1573,7 @@ bool Clipper::ExecuteInternal() noexcept {
 
 void Clipper::SetWindingCount(TEdge &edge) noexcept {
   TEdge *e = edge.PrevInAEL;
-  // find the edge of the same polytype that immediately preceeds 'edge' in AEL
+  // find the edge of the same polytype that immediately precedes 'edge' in AEL
   while (e && ((e->PolyTyp != edge.PolyTyp) || (e->WindDelta == 0)))
     e = e->PrevInAEL;
   if (!e) {

deploy/cpp_infer/src/postprocess_op.cpp

@@ -390,7 +390,7 @@ void TablePostProcessor::Run(
     const std::vector<int> &width_list,
     const std::vector<int> &height_list) noexcept {
   for (int batch_idx = 0; batch_idx < structure_probs_shape[0]; ++batch_idx) {
-    // image tags and boxs
+    // image tags and boxes
     std::vector<std::string> rec_html_tags;
     std::vector<std::vector<int>> rec_boxes;
 

deploy/fastdeploy/android/README.md

@@ -18,7 +18,7 @@
 </p>
 
 > **注意：**
->> 如果您在导入项目、编译或者运行过程中遇到 NDK 配置错误的提示，请打开 ` File > Project Structure > SDK Location`，修改 `Andriod SDK location` 为您本机配置的 SDK 所在路径。
+>> 如果您在导入项目、编译或者运行过程中遇到 NDK 配置错误的提示，请打开 ` File > Project Structure > SDK Location`，修改 `Android SDK location` 为您本机配置的 SDK 所在路径。
 
 4. 点击 Run 按钮，自动编译 APP 并安装到手机。(该过程会自动下载预编译的 FastDeploy Android 库 以及 模型文件，需要联网)
    成功后效果如下，图一：APP 安装到手机；图二： APP 打开后的效果，会自动识别图片中的物体并标记；图三：APP设置选项，点击右上角的设置图片，可以设置不同选项进行体验。

deploy/pdserving/README.md

@@ -33,7 +33,7 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
   - [Environmental preparation](#environmental-preparation)
   - [Model conversion](#model-conversion)
   - [Paddle Serving pipeline deployment](#paddle-serving-pipeline-deployment)
-  - [Paddle Serving C++ deployment](#C++)
+  - [C++ Serving](#c-serving)
   - [WINDOWS Users](#windows-users)
   - [FAQ](#faq)
 
@@ -247,7 +247,7 @@ The C++ service deployment is the same as python in the environment setup and da
 
 ## WINDOWS Users
 
-Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Windows_Tutorial_EN.md)
+Windows does not support Pipeline Serving, if we want to launch paddle serving on Windows, we should use Web Service, for more information please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Windows_Tutorial_EN.md)
 
 
 **WINDOWS user can only use version 0.5.0 CPU Mode**

docs/algorithm/text_recognition/algorithm_rec_parseq.en.md

@@ -11,7 +11,7 @@ Paper:
 > Darwin Bautista, Rowel Atienza
 > ECCV, 2021
 
-Using real datasets (real) and synthetic datsets (synth) for training respectively，and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets.
+Using real datasets (real) and synthetic datasets (synth) for training respectively，and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets.
 
 - The real datasets include COCO-Text, RCTW17, Uber-Text, ArT, LSVT, MLT19, ReCTS, TextOCR and OpenVINO datasets.
 - The synthesis datasets include MJSynth and SynthText datasets.

docs/datasets/kie_datasets.en.md

@@ -43,5 +43,5 @@ Here are the common datasets key information extraction, which are being updated
 **Note：** Boxes with category `Ignore` or `Others` are not visualized here.
 
 - **Download address**：
-    - Offical dataset: [link](https://download.openmmlab.com/mmocr/data/wildreceipt.tar)
+    - Official dataset: [link](https://download.openmmlab.com/mmocr/data/wildreceipt.tar)
     - Dataset converted for PaddleOCR training process: [link](https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar)

docs/datasets/layout_datasets.en.md

@@ -5,7 +5,7 @@ comments: true
 
 ## Layout Analysis Dataset
 
-Here are the common datasets of layout anlysis, which are being updated continuously. Welcome to contribute datasets.
+Here are the common datasets of layout analysis, which are being updated continuously. Welcome to contribute datasets.
 
 Most of the layout analysis datasets are object detection datasets. In addition to open source datasets, you can also label or synthesize datasets using tools such as [labelme](https://github.com/wkentaro/labelme) and so on.
 
@@ -33,7 +33,7 @@ Most of the layout analysis datasets are object detection datasets. In addition
 - **Download address**: <https://github.com/buptlihang/CDLA>
 - **Note**: When you train detection model on CDLA dataset using [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/develop), you need to remove the label `__ignore__` and `_background_`.
 
-### 3、TableBank dataet
+### 3、TableBank dataset
 
 - **Data source**: <https://doc-analysis.github.io/tablebank-page/index.html>
 - **Data introduction**: TableBank dataset contains 2 types of document: Latex (187199 training images, 7265 validation images and 5719 testing images) and Word (73383 training images 2735 validation images and 2281 testing images). Some images and their annotations as shown below.

docs/infer_deploy/index.en.md

@@ -13,7 +13,7 @@ Paddle provides a variety of deployment schemes to meet the deployment requireme
 
 ![img](./images/deployment_en.jpg)
 
-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](./python_infer.en.md)
 - [C++ Inference](./cpp_infer.en.md)

docs/infer_deploy/paddle_js.en.md

@@ -19,7 +19,7 @@ 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|

docs/infer_deploy/paddle_server.en.md

@@ -250,7 +250,7 @@ The C++ service deployment is the same as python in the environment setup and da
 
 ### WINDOWS Users
 
-Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Windows_Tutorial_EN.md)
+Windows does not support Pipeline Serving, if we want to launch paddle serving on Windows, we should use Web Service, for more information please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Windows_Tutorial_EN.md)
 
 **WINDOWS user can only use version 0.5.0 CPU Mode**
 

docs/ppocr/blog/whl.en.md

@@ -480,7 +480,7 @@ for idx in range(len(result)):
 | enable_mkldnn           | Whether to enable mkldnn                                                                                                                                                                                                       | FALSE                   |
 | use_zero_copy_run           | Whether to forward by zero_copy_run                                                                                                                                                                               | FALSE                   |
 | lang                     | The support language, now only Chinese(ch)、English(en)、French(french)、German(german)、Korean(korean)、Japanese(japan) are supported                                                                                                                                                                                                  | ch                    |
-| det                     | Enable detction when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |
+| det                     | Enable detection when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |
 | rec                     | Enable recognition when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |
 | cls                     | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction)                                                                                                                                                                                                   | FALSE                    |
 | show_log                     | Whether to print log| FALSE                    |

docs/ppocr/model_compress/knowledge_distillation.en.md

@@ -316,7 +316,7 @@ Metric:
   base_metric_name: RecMetric      # The base class of indicator calculation. For the output of the model, the indicator will be calculated based on this class
   main_indicator: acc              # The name of the indicator
   key: "Student"                   # Select the main_indicator of this subnet as the criterion for saving the best model
-  ignore_space: False              # whether to ignore space during evaulation
+  ignore_space: False              # whether to ignore space during evaluation
 ```
 
 Taking the above configuration as an example, the accuracy metric of the `Student` subnet will be used as the judgment metric for saving the best model.

docs/ppocr/model_train/detection.en.md

@@ -130,7 +130,7 @@ After adding the four-part modules of the network, you only need to configure th
     args1: args1
 ```
 
-**NOTE**: More details about replace Backbone and other mudule can be found in [doc](../../algorithm/add_new_algorithm.en.md).
+**NOTE**: More details about replace Backbone and other module can be found in [doc](../../algorithm/add_new_algorithm.en.md).
 
 ### 2.4 Mixed Precision Training
 

docs/ppocr/model_train/kie.en.md

@@ -93,7 +93,7 @@ The final dataset shall have the following file structure.
 
 ### 1.3. Download data
 
-If you do not have local dataset, you can donwload the source files of [XFUND](https://github.com/doc-analysis/XFUND) or [FUNSD](https://guillaumejaume.github.io/FUNSD) and use the scripts of [XFUND](../../ppstructure/kie/tools/trans_xfun_data.py) or [FUNSD](../../ppstructure/kie/tools/trans_funsd_label.py) for tranform them into PaddleOCR format. Then you can use the public dataset to quick experience KIE.
+If you do not have local dataset, you can download the source files of [XFUND](https://github.com/doc-analysis/XFUND) or [FUNSD](https://guillaumejaume.github.io/FUNSD) and use the scripts of [XFUND](../../ppstructure/kie/tools/trans_xfun_data.py) or [FUNSD](../../ppstructure/kie/tools/trans_funsd_label.py) for transform them into PaddleOCR format. Then you can use the public dataset to quick experience KIE.
 
 For more information about public KIE datasets, please refer to [KIE dataset tutorial](../../datasets/kie_datasets.en.md).
 
@@ -191,7 +191,7 @@ Architecture:
     name: LayoutXLMForSer
     pretrained: True
     mode: vi
-    # Assuming that n categroies are included in the dictionary file (other is included), the the num_classes is set as 2n-1
+    # Assuming that n categories are included in the dictionary file (other is included), the the num_classes is set as 2n-1
     num_classes: &num_classes 7
 
 PostProcess:
@@ -239,7 +239,7 @@ python3 tools/train.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml
 
 **Note:**
 
-- Priority of `Architecture.Backbone.checkpoints` is higher than`Architecture.Backbone.pretrained`. You need to set `Architecture.Backbone.checkpoints` for model finetuning, resume and evalution. If you want to train with the NLP pretrained model, you need to set `Architecture.Backbone.pretrained` as `True` and set `Architecture.Backbone.checkpoints` as null (`null`).
+- Priority of `Architecture.Backbone.checkpoints` is higher than`Architecture.Backbone.pretrained`. You need to set `Architecture.Backbone.checkpoints` for model finetuning, resume and evaluation. If you want to train with the NLP pretrained model, you need to set `Architecture.Backbone.pretrained` as `True` and set `Architecture.Backbone.checkpoints` as null (`null`).
 - PaddleNLP pretrained models are used here for LayoutXLM series models, the model loading and saving logic is same as those in PaddleNLP. Therefore we do not need to set `Global.pretrained_model` or `Global.checkpoints` here.
 - If you use knowledge distillation to train the LayoutXLM series models, resuming training is not supported now.
 
@@ -280,7 +280,7 @@ Running on a DCU device requires setting the environment variable `export HIP_VI
 
 ### 3.1. Evaluation
 
-The trained model will be saved in `Global.save_model_dir`. When evaluation, you need to set `Architecture.Backbone.checkpoints` as your model directroy. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` file.
+The trained model will be saved in `Global.save_model_dir`. When evaluation, you need to set `Architecture.Backbone.checkpoints` as your model directory. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml` file.
 
 ```bash linenums="1"
 # GPU evaluation, Global.checkpoints is the weight to be tested

docs/ppocr/model_train/recognition.en.md

@@ -325,7 +325,7 @@ After adding the four-part modules of the network, you only need to configure th
     args1: args1
 ```
 
-**NOTE**: More details about replace Backbone and other mudule can be found in [doc](../../algorithm/add_new_algorithm.en.md).
+**NOTE**: More details about replace Backbone and other module can be found in [doc](../../algorithm/add_new_algorithm.en.md).
 
 ### 2.4. Mixed Precision Training
 

docs/ppstructure/blog/how_to_do_kie.en.md

@@ -47,7 +47,7 @@ In the non end-to-end KIE method, KIE needs at least **2 steps**. Firstly, the O
 
 ##### (1) Data
 
-Most of the models provided in PaddleOCR are general models. In the process of text detection, the detection of adjacent text lines is generally based on the distance of the position. As shown in the figure above, when using PP-OCRv3 general English detection model for text detection, it is easy to detect the two fields representing different propoerties as one. Therefore, it is suggested to finetune a detection model according to your scenario firstly during the KIE task.
+Most of the models provided in PaddleOCR are general models. In the process of text detection, the detection of adjacent text lines is generally based on the distance of the position. As shown in the figure above, when using PP-OCRv3 general English detection model for text detection, it is easy to detect the two fields representing different properties as one. Therefore, it is suggested to finetune a detection model according to your scenario firstly during the KIE task.
 
 During data annotation, the different key information needs to be separated. Otherwise, it will increase the difficulty of subsequent KIE tasks.
 

docs/ppstructure/blog/return_word_pos.md

@@ -60,7 +60,7 @@ cd PaddleOCR/ppstructure
 
 ## download model
 cd inference
-## Download the detection model of the ultra-lightweight Chinesse PP-OCRv3 model and unzip it
+## Download the detection model of the ultra-lightweight Chinese PP-OCRv3 model and unzip it
 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_det_infer.tar
 ## Download the recognition model of the ultra-lightweight Chinese PP-OCRv3 model and unzip it
 wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar && tar xf ch_PP-OCRv3_rec_infer.tar

docs/ppstructure/model_train/recovery_to_doc.en.md

@@ -81,7 +81,7 @@ pip3 install pdf2docx-0.0.0-py3-none-any.whl
 
 ## 3. Quick Start using standard PDF parse
 
-`use_pdf2docx_api` use PDF parse for layout recovery, The whl package is also provided  for quick use, follow the above code, for more infomation please refer to [quickstart](../quick_start.en.md) for details.
+`use_pdf2docx_api` use PDF parse for layout recovery, The whl package is also provided  for quick use, follow the above code, for more information please refer to [quickstart](../quick_start.en.md) for details.
 
 ```bash linenums="1"
 # install paddleocr
@@ -110,7 +110,7 @@ Through layout analysis, we divided the image/PDF documents into regions, locate
 
 We can restore the test picture through the layout information, OCR detection and recognition structure, table information, and saved pictures.
 
-The whl package is also provided for quick use, follow the above code, for more infomation please refer to [quickstart](../quick_start.en.md) for details.
+The whl package is also provided for quick use, follow the above code, for more information please refer to [quickstart](../quick_start.en.md) for details.
 
 ```bash linenums="1"
 paddleocr --image_dir=ppstructure/docs/table/1.png --type=structure --recovery=true --lang='en'
@@ -166,8 +166,8 @@ Field：
 - det_model_dir：OCR detection model path
 - rec_model_dir：OCR recognition model path
 - rec_char_dict_path：OCR recognition dict path. If the Chinese model is used, change to "../ppocr/utils/ppocr_keys_v1.txt". And if you trained the model on your own dataset, change to the trained dictionary
-- table_model_dir：tabel recognition model path
-- table_char_dict_path：tabel recognition dict path. If the Chinese model is used, no need to change
+- table_model_dir：table recognition model path
+- table_char_dict_path：table recognition dict path. If the Chinese model is used, no need to change
 - layout_model_dir：layout analysis model path
 - layout_dict_path：layout analysis dict path. If the Chinese model is used, change to "../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt"
 - recovery：whether to enable layout of recovery, default False

ppocr/data/imaug/east_process.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-This code is refered from:
+This code is referred from:
 https://github.com/songdejia/EAST/blob/master/data_utils.py
 """
 import math
@@ -241,7 +241,7 @@ class EASTProcessTrain(object):
         score_map = np.zeros((h, w), dtype=np.uint8)
         # (x1, y1, ..., x4, y4, short_edge_norm)
         geo_map = np.zeros((h, w, 9), dtype=np.float32)
-        # mask used during traning, to ignore some hard areas
+        # mask used during training, to ignore some hard areas
         training_mask = np.ones((h, w), dtype=np.uint8)
         for poly_idx, poly_tag in enumerate(zip(polys, tags)):
             poly = poly_tag[0]

ppocr/data/imaug/operators.py

@@ -60,7 +60,7 @@ class DecodeImage(object):
 
 
 class NormalizeImage(object):
-    """normalize image such as substract mean, divide std"""
+    """normalize image such as subtract mean, divide std"""
 
     def __init__(self, scale=None, mean=None, std=None, order="chw", **kwargs):
         if isinstance(scale, str):

ppocr/data/imaug/pg_process.py

@@ -763,7 +763,7 @@ class PGProcessTrain(object):
 
     def prepare_text_label(self, label_str, Lexicon_Table):
         """
-        Prepare text lablel by given Lexicon_Table.
+        Prepare text label by given Lexicon_Table.
         """
         if len(Lexicon_Table) == 36:
             return label_str.lower()

ppocr/data/imaug/rec_img_aug.py

@@ -413,7 +413,7 @@ class SARRecResizeImg(object):
 class PRENResizeImg(object):
     def __init__(self, image_shape, **kwargs):
         """
-        Accroding to original paper's realization, it's a hard resize method here.
+        According to original paper's realization, it's a hard resize method here.
         So maybe you should optimize it to fit for your task better.
         """
         self.dst_h, self.dst_w = image_shape

ppocr/data/imaug/sast_process.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-This part code is refered from:
+This part code is referred from:
 https://github.com/songdejia/EAST/blob/master/data_utils.py
 """
 import math

ppocr/data/simple_dataset.py

@@ -206,7 +206,7 @@ class MultiScaleDataSet(SimpleDataSet):
         return data
 
     def __getitem__(self, properties):
-        # properites is a tuple, contains (width, height, index)
+        # properties is a tuple, contains (width, height, index)
         img_height = properties[1]
         idx = properties[2]
         if self.ds_width and properties[3] is not None:

ppocr/losses/det_db_loss.py

@@ -30,7 +30,7 @@ class DBLoss(nn.Layer):
     """
     Differentiable Binarization (DB) Loss Function
     args:
-        param (dict): the super paramter for DB Loss
+        param (dict): the super parameter for DB Loss
     """
 
     def __init__(

ppocr/losses/distillation_loss.py

@@ -1016,7 +1016,7 @@ class CTCDKDLoss(nn.Layer):
         targets = targets.astype("int32")
         res = F.one_hot(targets, num_classes=11465)
         mask = paddle.clip(paddle.sum(res, axis=1), 0, 1)
-        mask[:, 0] = 0  # ingore ctc blank label
+        mask[:, 0] = 0  # ignore ctc blank label
         return mask
 
     def forward(self, logits_student, logits_teacher, targets, mask=None):
@@ -1128,7 +1128,7 @@ class KLCTCLogits(nn.Layer):
             tea_out *= blank_mask
             return self.forward_meanlog(stu_out, tea_out)
         elif self.mode == "ctcdkd":
-            # ingore ctc blank logits
+            # ignore ctc blank logits
             blank_mask = paddle.ones_like(stu_out)
             blank_mask.stop_gradient = True
             blank_mask[:, :, 0] = -1

ppocr/metrics/ct_metric.py

@@ -34,7 +34,7 @@ class CTMetric(object):
 
     def __call__(self, preds, batch, **kwargs):
         # NOTE: only support bs=1 now, as the label length of different sample is Unequal
-        assert len(preds) == 1, "CentripetalText test now only suuport batch_size=1."
+        assert len(preds) == 1, "CentripetalText test now only support batch_size=1."
         label = batch[2]
         text = batch[3]
         pred = preds[0]["points"]

ppocr/modeling/architectures/base_model.py

@@ -34,10 +34,10 @@ class BaseModel(nn.Layer):
         super(BaseModel, self).__init__()
         in_channels = config.get("in_channels", 3)
         model_type = config["model_type"]
-        # build transfrom,
-        # for rec, transfrom can be TPS,None
-        # for det and cls, transfrom shoule to be None,
-        # if you make model differently, you can use transfrom in det and cls
+        # build transform,
+        # for rec, transform can be TPS,None
+        # for det and cls, transform shoule to be None,
+        # if you make model differently, you can use transform in det and cls
         if "Transform" not in config or config["Transform"] is None:
             self.use_transform = False
         else:

ppocr/modeling/necks/db_fpn.py

@@ -428,7 +428,7 @@ class LKPAN(nn.Layer):
 
 class ASFBlock(nn.Layer):
     """
-    This code is refered from:
+    This code is referred from:
         https://github.com/MhLiao/DB/blob/master/decoders/feature_attention.py
     """
 

ppocr/modeling/transforms/gaspin_transformer.py

@@ -128,7 +128,7 @@ class GA_SPIN_Transformer(nn.Layer):
             default_type (int): the K chromatic space,
                                 set it to 3/5/6 depend on the complexity of transformation intensities
             loc_lr (float): learning rate of location network
-            stn (bool): whther to use stn.
+            stn (bool): whether to use stn.
 
         """
         super(GA_SPIN_Transformer, self).__init__()

ppocr/optimizer/lr_scheduler.py

@@ -49,7 +49,7 @@ class OneCycleDecay(LRScheduler):
     """
     One Cycle learning rate decay
     A learning rate which can be referred in https://arxiv.org/abs/1708.07120
-    Code refered in https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
+    Code referred in https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
     """
 
     def __init__(

ppocr/postprocess/ct_postprocess.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-This code is refered from:
+This code is referred from:
 https://github.com/shengtao96/CentripetalText/blob/main/test.py
 """
 

ppocr/postprocess/db_postprocess.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-This code is refered from:
+This code is referred from:
 https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py
 """
 from __future__ import absolute_import

ppocr/postprocess/locality_aware_nms.py

@@ -1,6 +1,6 @@
 """
 Locality aware nms.
-This code is refered from: https://github.com/songdejia/EAST/blob/master/locality_aware_nms.py
+This code is referred from: https://github.com/songdejia/EAST/blob/master/locality_aware_nms.py
 """
 
 import numpy as np
@@ -71,7 +71,7 @@ def standard_nms(S, thres):
 
 def standard_nms_inds(S, thres):
     """
-    Standard nms, retun inds.
+    Standard nms, return inds.
     """
     order = np.argsort(S[:, 8])[::-1]
     keep = []
@@ -158,8 +158,8 @@ def soft_nms(boxes_in, Nt_thres=0.3, threshold=0.8, sigma=0.5, method=2):
                     else:
                         weight = 1
                 boxes[pos, 8] = weight * boxes[pos, 8]
-                # if box score falls below thresold, discard the box by
-                # swaping last box update N
+                # if box score falls below threshold, discard the box by
+                # swapping last box update N
                 if boxes[pos, 8] < threshold:
                     boxes[pos, :] = boxes[N - 1, :]
                     inds[pos] = inds[N - 1]

ppocr/postprocess/rec_postprocess.py

@@ -80,8 +80,8 @@ class BaseRecLabelDecode(object):
             word_list: list of the grouped words
             word_col_list: list of decoding positions corresponding to each character in the grouped word
             state_list: list of marker to identify the type of grouping words, including two types of grouping words:
-                        - 'cn': continous chinese characters (e.g., 你好啊)
-                        - 'en&num': continous english characters (e.g., hello), number (e.g., 123, 1.123), or mixed of them connected by '-' (e.g., VGG-16)
+                        - 'cn': continuous chinese characters (e.g., 你好啊)
+                        - 'en&num': continuous english characters (e.g., hello), number (e.g., 123, 1.123), or mixed of them connected by '-' (e.g., VGG-16)
                         The remaining characters in text are treated as separators between groups (e.g., space, '(', ')', etc.).
         """
         state = None
@@ -105,7 +105,7 @@ class BaseRecLabelDecode(object):
                 and state == "en&num"
                 and c_i + 1 < len(text)
                 and bool(re.search("[0-9]", text[c_i + 1]))
-            ):  # grouping floting number
+            ):  # grouping floating number
                 c_state = "en&num"
             if (
                 char == "-" and state == "en&num"

ppocr/utils/dict/README.md

@@ -1,5 +1,5 @@
 ## Dictionary and Corpus
 
-Dictionary files (usually character level vocabulary) are included here for easier configuration. Corpus contributed by OSS contirbutors are listed here, please respect copyrights when using them at your own risk.
+Dictionary files (usually character level vocabulary) are included here for easier configuration. Corpus contributed by OSS contributors are listed here, please respect copyrights when using them at your own risk.
 
 - Burmese corpus:  https://github.com/1chimaruGin/BurmeseCorpus

ppocr/utils/e2e_utils/extract_textpoint_slow.py

@@ -386,7 +386,7 @@ def generate_pivot_list_curved(
                 pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
             all_pos_yxs.append(pos_list_sorted)
 
-    # use decoder to filter backgroud points.
+    # use decoder to filter background points.
     p_char_maps = p_char_maps.transpose([1, 2, 0])
     decode_res = ctc_decoder_for_image(
         all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True
@@ -464,7 +464,7 @@ def generate_pivot_list_horizontal(
             pos_list_sorted, _ = sort_with_direction(pos_list_final, f_direction)
             all_pos_yxs.append(pos_list_sorted)
 
-    # use decoder to filter backgroud points.
+    # use decoder to filter background points.
     p_char_maps = p_char_maps.transpose([1, 2, 0])
     decode_res = ctc_decoder_for_image(
         all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True

ppocr/utils/network.py

@@ -83,7 +83,7 @@ def _download(url, save_path):
                 "{}!".format(url, req.status_code)
             )
 
-        # For protecting download interupted, download to
+        # For protecting download interrupted, download to
         # tmp_file firstly, move tmp_file to save_path
         # after download finished
         tmp_file = save_path + ".tmp"

ppocr/utils/utility.py

@@ -180,7 +180,7 @@ def set_seed(seed=1024):
 def check_install(module_name, install_name):
     spec = importlib.util.find_spec(module_name)
     if spec is None:
-        print(f"Warnning! The {module_name} module is NOT installed")
+        print(f"Warning! The {module_name} module is NOT installed")
         print(
             f"Try install {module_name} module automatically. You can also try to install manually by pip install {install_name}."
         )