diff --git a/deploy/cpp_infer/src/ocr_rec.cpp b/deploy/cpp_infer/src/ocr_rec.cpp
index f1a97a99a3d3487988c35766592668ba3f43c784..25224f88acecd33f5efaa34a9dfc71639663d53f 100644
--- a/deploy/cpp_infer/src/ocr_rec.cpp
+++ b/deploy/cpp_infer/src/ocr_rec.cpp
@@ -15,108 +15,115 @@
 #include <include/ocr_rec.h>
 
 namespace PaddleOCR {
-    
-void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *times) {
-    std::chrono::duration<float> preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
-    std::chrono::duration<float> inference_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
-    std::chrono::duration<float> postprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
-
-    int img_num = img_list.size();
-    std::vector<float> width_list;
-    for (int i = 0; i < img_num; i++) {
-        width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
+
+void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
+                         std::vector<double> *times) {
+  std::chrono::duration<float> preprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> inference_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> postprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+
+  int img_num = img_list.size();
+  std::vector<float> width_list;
+  for (int i = 0; i < img_num; i++) {
+    width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
+  }
+  std::vector<int> indices = Utility::argsort(width_list);
+
+  for (int beg_img_no = 0; beg_img_no < img_num;
+       beg_img_no += this->rec_batch_num_) {
+    auto preprocess_start = std::chrono::steady_clock::now();
+    int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
+    float max_wh_ratio = 0;
+    for (int ino = beg_img_no; ino < end_img_no; ino++) {
+      int h = img_list[indices[ino]].rows;
+      int w = img_list[indices[ino]].cols;
+      float wh_ratio = w * 1.0 / h;
+      max_wh_ratio = max(max_wh_ratio, wh_ratio);
     }
-    std::vector<int> indices = Utility::argsort(width_list);
-
-    for (int beg_img_no = 0; beg_img_no < img_num; beg_img_no += this->rec_batch_num_) {
-        auto preprocess_start = std::chrono::steady_clock::now();
-        int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
-        float max_wh_ratio = 0;
-        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
-            int h = img_list[indices[ino]].rows;
-            int w = img_list[indices[ino]].cols;
-            float wh_ratio = w * 1.0 / h;
-            max_wh_ratio = max(max_wh_ratio, wh_ratio);
-        }
-        std::vector<cv::Mat> norm_img_batch;
-        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
-            cv::Mat srcimg;
-            img_list[indices[ino]].copyTo(srcimg);
-            cv::Mat resize_img;
-            this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, this->use_tensorrt_);
-            this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_);
-            norm_img_batch.push_back(resize_img);
-        }
-        
-        int batch_width = int(ceilf(32 * max_wh_ratio)) - 1;
-        std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
-        this->permute_op_.Run(norm_img_batch, input.data());
-        auto preprocess_end = std::chrono::steady_clock::now();
-        preprocess_diff += preprocess_end - preprocess_start;
-
-        // Inference.
-        auto input_names = this->predictor_->GetInputNames();
-        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
-        input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
-        auto inference_start = std::chrono::steady_clock::now();
-        input_t->CopyFromCpu(input.data());
-        this->predictor_->Run();
-
-        std::vector<float> predict_batch;
-        auto output_names = this->predictor_->GetOutputNames();
-        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
-        auto predict_shape = output_t->shape();
-
-        int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
-                                std::multiplies<int>());
-        predict_batch.resize(out_num);
-
-        output_t->CopyToCpu(predict_batch.data());
-        auto inference_end = std::chrono::steady_clock::now();
-        inference_diff += inference_end - inference_start;
-        
-        // ctc decode
-        auto postprocess_start = std::chrono::steady_clock::now();
-        for (int m = 0; m < predict_shape[0]; m++) {
-            std::vector<std::string> str_res;
-            int argmax_idx;
-            int last_index = 0;
-            float score = 0.f;
-            int count = 0;
-            float max_value = 0.0f;
-
-            for (int n = 0; n < predict_shape[1]; n++) {
-                argmax_idx =
-                    int(Utility::argmax(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
-                                        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
-                max_value =
-                    float(*std::max_element(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
-                                            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
-
-                if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
-                    score += max_value;
-                    count += 1;
-                    str_res.push_back(label_list_[argmax_idx]);
-                }
-                last_index = argmax_idx;
-            }
-            score /= count;
-            if (isnan(score))
-                continue;
-            for (int i = 0; i < str_res.size(); i++) {
-                std::cout << str_res[i];
-            }
-            std::cout << "\tscore: " << score << std::endl;
+    int batch_width = 0;
+    std::vector<cv::Mat> norm_img_batch;
+    for (int ino = beg_img_no; ino < end_img_no; ino++) {
+      cv::Mat srcimg;
+      img_list[indices[ino]].copyTo(srcimg);
+      cv::Mat resize_img;
+      this->resize_op_.Run(srcimg, resize_img, max_wh_ratio,
+                           this->use_tensorrt_);
+      this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                              this->is_scale_);
+      norm_img_batch.push_back(resize_img);
+      batch_width = max(resize_img.cols, batch_width);
+    }
+
+    std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
+    this->permute_op_.Run(norm_img_batch, input.data());
+    auto preprocess_end = std::chrono::steady_clock::now();
+    preprocess_diff += preprocess_end - preprocess_start;
+
+    // Inference.
+    auto input_names = this->predictor_->GetInputNames();
+    auto input_t = this->predictor_->GetInputHandle(input_names[0]);
+    input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
+    auto inference_start = std::chrono::steady_clock::now();
+    input_t->CopyFromCpu(input.data());
+    this->predictor_->Run();
+
+    std::vector<float> predict_batch;
+    auto output_names = this->predictor_->GetOutputNames();
+    auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
+    auto predict_shape = output_t->shape();
+
+    int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
+                                  std::multiplies<int>());
+    predict_batch.resize(out_num);
+
+    output_t->CopyToCpu(predict_batch.data());
+    auto inference_end = std::chrono::steady_clock::now();
+    inference_diff += inference_end - inference_start;
+
+    // ctc decode
+    auto postprocess_start = std::chrono::steady_clock::now();
+    for (int m = 0; m < predict_shape[0]; m++) {
+      std::vector<std::string> str_res;
+      int argmax_idx;
+      int last_index = 0;
+      float score = 0.f;
+      int count = 0;
+      float max_value = 0.0f;
+
+      for (int n = 0; n < predict_shape[1]; n++) {
+        argmax_idx = int(Utility::argmax(
+            &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
+        max_value = float(*std::max_element(
+            &predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
+
+        if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
+          score += max_value;
+          count += 1;
+          str_res.push_back(label_list_[argmax_idx]);
         }
-        auto postprocess_end = std::chrono::steady_clock::now();
-        postprocess_diff += postprocess_end - postprocess_start;
+        last_index = argmax_idx;
+      }
+      score /= count;
+      if (isnan(score))
+        continue;
+      for (int i = 0; i < str_res.size(); i++) {
+        std::cout << str_res[i];
+      }
+      std::cout << "\tscore: " << score << std::endl;
     }
-    times->push_back(double(preprocess_diff.count() * 1000));
-    times->push_back(double(inference_diff.count() * 1000));
-    times->push_back(double(postprocess_diff.count() * 1000));
+    auto postprocess_end = std::chrono::steady_clock::now();
+    postprocess_diff += postprocess_end - postprocess_start;
+  }
+  times->push_back(double(preprocess_diff.count() * 1000));
+  times->push_back(double(inference_diff.count() * 1000));
+  times->push_back(double(postprocess_diff.count() * 1000));
 }
 
-    
 void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   //   AnalysisConfig config;
   paddle_infer::Config config;
@@ -130,23 +137,17 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
       if (this->precision_ == "fp16") {
         precision = paddle_infer::Config::Precision::kHalf;
       }
-     if (this->precision_ == "int8") {
+      if (this->precision_ == "int8") {
         precision = paddle_infer::Config::Precision::kInt8;
-      } 
-      config.EnableTensorRtEngine(
-          1 << 20, 10, 3,
-          precision,
-          false, false);
+      }
+      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
 
       std::map<std::string, std::vector<int>> min_input_shape = {
-          {"x", {1, 3, 32, 10}},
-          {"lstm_0.tmp_0", {10, 1, 96}}};
+          {"x", {1, 3, 32, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}};
       std::map<std::string, std::vector<int>> max_input_shape = {
-          {"x", {1, 3, 32, 2000}},
-          {"lstm_0.tmp_0", {1000, 1, 96}}};
+          {"x", {1, 3, 32, 2000}}, {"lstm_0.tmp_0", {1000, 1, 96}}};
       std::map<std::string, std::vector<int>> opt_input_shape = {
-          {"x", {1, 3, 32, 320}},
-          {"lstm_0.tmp_0", {25, 1, 96}}};
+          {"x", {1, 3, 32, 320}}, {"lstm_0.tmp_0", {25, 1, 96}}};
 
       config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                                     opt_input_shape);
@@ -168,7 +169,7 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   config.SwitchIrOptim(true);
 
   config.EnableMemoryOptim();
-//   config.DisableGlogInfo();
+  //   config.DisableGlogInfo();
 
   this->predictor_ = CreatePredictor(config);
 }