There has been a wide advancement in technology over the past two decades and the extensively usage of camera-embedded mobile phones all around the world has raised the demand of PC vision tasks and offers rise to incalculable open entryways in the field. A hot emerging area of research in this field is the extraction of text embedded in natural scene images. Natural scene images are the photos taken from a camera, where the establishment is self-assertive, and the arrangement of tones used in the image is classy and unique. Exactly when text is accessible in such sort of pictures, it is typically difficult for a machine to recognize and eliminate this due to different limits. This paper deals with a method that uses a blend of the Open Source Computer Vision Library (OpenCV) and the architectures of Machine Learning (ML), to separate English text from images. This paper provides the result by using two Machine Learning architectures, namely, Multi Layer Perceptron (MLP) and TensorFlow (TF). The data is predicted by using both the architectures, to compare their model accuracies and analyze their problems in text detection. The proposed OpenCV execution accomplished a precision rate of 95.2% and a recall rate of 70.6%, proving the efficacy of approach utilized.


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    Title :

    New Text Detection Technique Using Machine Learning Architecture


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:


    Publication date :

    2022-10-05


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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