Image recognition by deep learning
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Object recognition has become a crucial topic in the field of computer vision. Poor qualities of images unable bring out the desired object as per expectancy. Many models have proposed to recognize object from image. However, most of these approaches hardly achieve high accuracy and precision. It creates a major obstacle to get correctness of the research because of the lighting, illumination, image quality, noise, ethnicity and various angels of similar objects. Therefore, we have proposed a novel approach to detect any object by CNN method including HAAR Cascade classifier where we first detect the most prominent features from scene using Haar Feature Based Cascade Classifier that has been introduced by Paul Viola and Michael Jones. In the second phase, the classification has been used for Convolutional Neural Network to detect the object automatically with better accuracy and more efficiently. It can determine any object after proper training and dataset manipulation. Our proposed method for image recognition has achieved very good accuracy than our expectation.