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Implement ctc prefix beam search decode for TextRecognitionModel.

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The algorithm is based on Hannun's paper: First-Pass Large Vocabulary
Continuous Speech Recognition using Bi-Directional Recurrent DNNs
This commit is contained in:
JIANG Yichen
2021-08-09 13:46:11 +08:00
parent ea068dcc2c
commit 955cf35d5f
5 changed files with 335 additions and 39 deletions
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modules/dnn/test/test_model.cpp
+19
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@@ -615,6 +615,25 @@ TEST_P(Test_Model, TextRecognition)
testTextRecognitionModel(weightPath, "", imgPath, seq, decodeType, vocabulary, size, mean, scale);
}
TEST_P(Test_Model, TextRecognitionWithCTCPrefixBeamSearch)
{
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
std::string imgPath = _tf("text_rec_test.png");
std::string weightPath = _tf("onnx/models/crnn.onnx", false);
std::string seq = "welcome";
Size size{100, 32};
double scale = 1.0 / 127.5;
Scalar mean = Scalar(127.5);
std::string decodeType = "CTC-prefix-beam-search";
std::vector<std::string> vocabulary = {"0","1","2","3","4","5","6","7","8","9",
"a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"};
testTextRecognitionModel(weightPath, "", imgPath, seq, decodeType, vocabulary, size, mean, scale);
}
TEST_P(Test_Model, TextDetectionByDB)
{
if (target == DNN_TARGET_OPENCL_FP16)