An Efficient Object Detection Model Using Convolution Neural Networks

Image processing and computer vision have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Object detection and Scene Recognition. Though there are numerous existing works related to the specified fields object detection still encounters numerous challenges when it comes to implementing in the real-time scenario. The problem occurs in the detection due to various objects present in the background. Object detection mechanism detects a specified object when a particular scene is given. Classifiers like SVM and Neural Networks are used to train the classifier in such a way they are able to detect an object when a new image is given. In this paper, we have proposed a model which detects texts from an image. Bounding boxes are used to detect the texts and localize it. The neural network is used to train the model where numerous images having texts are given as the training set. The performance evaluation is done on the model and it is observed that it detects the texts when a new image is given. Object detection is a fundamental problem in computer vision, which aims to detect general objects in images.