Fix typos (#14808)

README_en.md

@@ -31,7 +31,7 @@ PaddleOCR is being oversight by a [PMC](https://github.com/PaddlePaddle/PaddleOC
 
 - **🔥 2024.10.18 release PaddleOCR v2.9, including**:
     - PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
-        - 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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 **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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**.
 

benchmark/PaddleOCR_DBNet/data_loader/modules/make_shrink_map.py

@@ -24,12 +24,12 @@ def shrink_polygon_pyclipper(polygon, shrink_ratio):
     subject = [tuple(l) for l in polygon]
     padding = pyclipper.PyclipperOffset()
     padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
-    shrinked = padding.Execute(-distance)
-    if shrinked == []:
-        shrinked = np.array(shrinked)
+    shrunk = padding.Execute(-distance)
+    if shrunk == []:
+        shrunk = np.array(shrunk)
     else:
-        shrinked = np.array(shrinked[0]).reshape(-1, 2)
-    return shrinked
+        shrunk = np.array(shrunk[0]).reshape(-1, 2)
+    return shrunk
 
 
 class MakeShrinkMap:
@@ -69,12 +69,12 @@ class MakeShrinkMap:
                 cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0)
                 ignore_tags[i] = True
             else:
-                shrinked = self.shrink_func(polygon, self.shrink_ratio)
-                if shrinked.size == 0:
+                shrunk = self.shrink_func(polygon, self.shrink_ratio)
+                if shrunk.size == 0:
                     cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0)
                     ignore_tags[i] = True
                     continue
-                cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1)
+                cv2.fillPoly(gt, [shrunk.astype(np.int32)], 1)
 
         data["shrink_map"] = gt
         data["shrink_mask"] = mask

deploy/cpp_infer/src/args.cpp

@@ -15,10 +15,10 @@
 #include <gflags/gflags.h>
 
 // common args
-DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
+DEFINE_bool(use_gpu, false, "Inferring with GPU or CPU.");
 DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
 DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
-DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
+DEFINE_int32(gpu_mem, 4000, "GPU id when inferring with GPU.");
 DEFINE_int32(cpu_threads, 10, "Num of threads with CPU.");
 DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU.");
 DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8");

docs/applications/光功率计数码管字符识别.md

@@ -53,7 +53,7 @@ python3 -m pip install paddleocr
 ![](./images/8dca91f016884e16ad9216d416da72ea08190f97d87b4be883f15079b7ebab9a.jpeg)
 
 ```bash linenums="1"
-paddleocr --lang=ch --det=Fase --image_dir=data
+paddleocr --lang=ch --det=False --image_dir=data
 ```
 
 得到如下测试结果：

docs/applications/液晶屏读数识别.md

@@ -182,7 +182,7 @@ python tools/eval.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o G
 
 ```python linenums="1"
 cd ./pretrained_models/
-# transform teacher params in best_accuracy.pdparams into teacher_dml.paramers
+# transform teacher params in best_accuracy.pdparams into teacher_dml.pdparams
 import paddle
 
 # load pretrained model

docs/index.en.md

@@ -32,7 +32,7 @@ PaddleOCR is being oversight by a [PMC](https://github.com/PaddlePaddle/PaddleOC
 
 - **🔥 2024.10.18 release PaddleOCR v2.9, including**:
     - PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
-        - 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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 **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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**.
 

docs/paddlex/overview.en.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 **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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 **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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**.
 

docs/ppocr/model_train/tricks.en.md

@@ -60,4 +60,4 @@ Here we have sorted out some Chinese OCR training and prediction tricks, which a
 
 - **Tips**
 
-  There are two possible methods for space recognition. (1) Optimize the text detection. For spliting the text at the space in detection results, it needs to divide the text line with space into many segments when label the data for detection. (2) Optimize the text recognition. The space character is introduced into the recognition dictionary. Label the blank line in the training data for text recognition. In addition, we can also concat multiple word lines to synthesize the training data with spaces. PaddleOCR currently uses the second method.
+  There are two possible methods for space recognition. (1) Optimize the text detection. For splitting the text at the space in detection results, it needs to divide the text line with space into many segments when label the data for detection. (2) Optimize the text recognition. The space character is introduced into the recognition dictionary. Label the blank line in the training data for text recognition. In addition, we can also concat multiple word lines to synthesize the training data with spaces. PaddleOCR currently uses the second method.

docs/ppstructure/infer_deploy/index.en.md

@@ -10,7 +10,7 @@ Paddle provides a variety of deployment schemes to meet the deployment requireme
 
 ![img](./images/deployment-20240704135743247.png)
 
-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/update.en.md

@@ -9,7 +9,7 @@ hide:
 
 - **🔥 2024.10.18 release PaddleOCR v2.9, including**:
   * PaddleX, an All-in-One development tool based on PaddleOCR's advanced technology, supports low-code full-process development capabilities in the OCR field:
-     * 🎨 [**Rich Model One-Click Call**](https://paddlepaddle.github.io/PaddleOCR/latest/en/paddlex/quick_start.html): Integrates **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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 **17 models** related to text image intelligent analysis, general OCR, general layout parsing, table recognition, formula recognition, and seal recognition into 6 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 20+ 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**.
 

ppocr/data/imaug/east_process.py

@@ -169,7 +169,7 @@ class EASTProcessTrain(object):
         used for generate the score map
         :param poly: the text poly
         :param r: r in the paper
-        :return: the shrinked poly
+        :return: the shrunk poly
         """
         # shrink ratio
         R = 0.3

ppocr/data/imaug/make_pse_gt.py

@@ -88,17 +88,17 @@ class MakePseGt(object):
             subject = [tuple(l) for l in poly]
             pco = pyclipper.PyclipperOffset()
             pco.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
-            shrinked = np.array(pco.Execute(-distance))
+            shrunk = np.array(pco.Execute(-distance))
 
-            if len(shrinked) == 0 or shrinked.size == 0:
+            if len(shrunk) == 0 or shrunk.size == 0:
                 if ignore_tags is not None:
                     ignore_tags[i] = True
                 continue
             try:
-                shrinked = np.array(shrinked[0]).reshape(-1, 2)
+                shrunk = np.array(shrunk[0]).reshape(-1, 2)
             except:
                 if ignore_tags is not None:
                     ignore_tags[i] = True
                 continue
-            cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1)
+            cv2.fillPoly(text_kernel, [shrunk.astype(np.int32)], i + 1)
         return text_kernel, ignore_tags

ppocr/data/imaug/make_shrink_map.py

@@ -64,7 +64,7 @@ class MakeShrinkMap(object):
                 subject = [tuple(l) for l in polygon]
                 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, self.shrink_ratio)
@@ -77,18 +77,18 @@ class MakeShrinkMap(object):
                         * (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)
 
         data["shrink_map"] = gt
         data["shrink_mask"] = mask

ppocr/modeling/heads/rec_aster_head.py

@@ -48,13 +48,13 @@ class AsterHead(nn.Layer):
             in_channels, out_channels, sDim, attDim, max_len_labels
         )
         self.time_step = time_step
-        self.embeder = Embedding(self.time_step, in_channels)
+        self.embedder = Embedding(self.time_step, in_channels)
         self.beam_width = beam_width
         self.eos = self.num_classes - 3
 
     def forward(self, x, targets=None, embed=None):
         return_dict = {}
-        embedding_vectors = self.embeder(x)
+        embedding_vectors = self.embedder(x)
 
         if self.training:
             rec_targets, rec_lengths, _ = targets

ppocr/modeling/heads/self_attention.py

@@ -166,7 +166,7 @@ class Encoder(nn.Layer):
                     ),
                 )
             )
-        self.processer = PrePostProcessLayer(
+        self.processor = PrePostProcessLayer(
             preprocess_cmd, d_model, prepostprocess_dropout
         )
 
@@ -174,7 +174,7 @@ class Encoder(nn.Layer):
         for encoder_layer in self.encoder_layers:
             enc_output = encoder_layer(enc_input, attn_bias)
             enc_input = enc_output
-        enc_output = self.processer(enc_output)
+        enc_output = self.processor(enc_output)
         return enc_output
 
 

ppstructure/pdf2word/pdf2word.py

@@ -508,7 +508,7 @@ class APP_Image2Doc(QWidget):
         self.pb.setRange(0, max)
 
     def handleEndsignalSignal(self):
-        # enble buttons
+        # enable buttons
         self.openFileButton.setEnabled(True)
         self.startCNButton.setEnabled(True)
         self.startENButton.setEnabled(True)

test_tipc/test_inference_cpp.sh

@@ -66,7 +66,7 @@ function func_cpp_inference(){
                     for batch_size in ${cpp_batch_size_list[*]}; do
                         precision="fp32"
                         if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                            precison="int8"
+                            precision="int8"
                         fi
                         _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
                         set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")