diff --git a/modules/ml/include/opencv2/ml/ml.hpp b/modules/ml/include/opencv2/ml/ml.hpp
index 790aea9014..8def877a59 100644
--- a/modules/ml/include/opencv2/ml/ml.hpp
+++ b/modules/ml/include/opencv2/ml/ml.hpp
@@ -488,7 +488,7 @@ public:
         bool balanced=false );
 
     virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
-    virtual float predict( const CvMat* samples, CvMat* results ) const;
+    virtual float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
 
 #ifndef SWIG
     CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
@@ -510,6 +510,7 @@ public:
                             CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE),
                             bool balanced=false);
     CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
+    CV_WRAP_AS(predict_all) virtual void predict( cv::InputArray samples, cv::OutputArray results ) const;
 #endif
 
     CV_WRAP virtual int get_support_vector_count() const;
diff --git a/modules/ml/src/svm.cpp b/modules/ml/src/svm.cpp
index d2bc88f053..9cbc46be84 100644
--- a/modules/ml/src/svm.cpp
+++ b/modules/ml/src/svm.cpp
@@ -2124,6 +2124,12 @@ float CvSVM::predict(const CvMat* samples, CV_OUT CvMat* results) const
     return result;
 }
 
+void CvSVM::predict( cv::InputArray _samples, cv::OutputArray _results ) const
+{
+    _results.create(_samples.size().height, 1, CV_32F);
+    CvMat samples = _samples.getMat(), results = _results.getMat();
+    predict(&samples, &results);
+}
 
 CvSVM::CvSVM( const Mat& _train_data, const Mat& _responses,
               const Mat& _var_idx, const Mat& _sample_idx, CvSVMParams _params )
diff --git a/samples/python2/letter_recog.py b/samples/python2/letter_recog.py
index 5611b92247..aabc6b9db1 100644
--- a/samples/python2/letter_recog.py
+++ b/samples/python2/letter_recog.py
@@ -88,7 +88,7 @@ class SVM(LetterStatModel):
         self.model.train(samples, responses, params = params)
 
     def predict(self, samples):
-        return np.float32( [self.model.predict(s) for s in samples] )
+        return self.model.predict_all(samples).ravel()
 
 
 class MLP(LetterStatModel):