International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 10 | Oct 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 40 Comparative Study of Different Techniques for Text as well as Object Detection from Real Time Images Monika Kapoor 1 , Er. Saurabh Sharma 2 1 Student, Dept of computer science Engineering, Sri Sai University Palampur (H.P) India 2 Assitant Professor, Dept. of Computer science and Engineering, Sri Sai University Palampur (H.P) India ---------------------------------------------------------------------------***--------------------------------------------------------------------------- Abstract - Since the introduction of artificial intelligence, it has been a major curiosity of the developers to make computers think more like humans. Artificial Neural Networks was developed with the view in mind to make computers to do so. These intelligent machines can be used in the field of robotics, medicines, industry. Since the introduction of image recognition algorithms by face-book AI researchers image identification and recognition has become a curios field among the developers. The main objective of the image identification software(s) is to differentiate the components of the image or the scene and tell them apart. Many algorithms such as OCR, RCNN, Mask RCNN, Fast RCNN, Faster RCNN algorithms were developed to identify and classify images into the categories as desired by the programmer. Here in this paper, we would throw some light on the aforementioned different types of Image Processing algorithms and try to determine which of these are best suited. Researchers have proved that Mask RCNN, which is a better version of Faster RCNN proved to the best suited algorithm in the field of object detection in the real time. Keywords: Artificial Neural Networks, Machine Learning, Object Identification, Scene Identification, OCR, Mask RCNN, RCNN. 1. INTRODUCTION Realworld applications are looking forward to make use of computers to make tasks easier for people in real world purposes. Due to its easiness in the usage computers are used in wide variety of fields such as robotics, medicine and advanced computing. Likewise, if robots are feed data about their surroundings then they can be better informed about their nearby environments to handle the situations. In a similarly way, if a computer can been taught how to differentiate and recognize tumor cells then it can be used for the identification of the tumors and cancer cells from the hundreds of pictures and can reduce the labor of manually identifying the tumors[1]. Likewise, in the case of the self-driven cars it becomes important for the system to identify the objects not only in the still images but in real time and take measures accordingly. Object detection aims to learn the concept of visual models in an image[2]. The ability to model the various deformations, inclusions, and other class variations while handling the large amount of data simultaneously under several conditions, is the main objective through this type of study. Machine Learning (ML) [3] is a branch of artificial intelligence that is targeted towards the training of machines; designing the algorithms to handle robots etc. these machines learn to operate on their own after training on datasets. Machines are taught to take data driven decisions through self-calculation and self-observation instead of being exclusively programmed for a special task they are programmed for performing a number of tasks simultaneously. The a self-iterating algorithm is developed in such a way that it learns and improves itself. If a new input data is encountered by the ML algorithm, it makes a forecast based on the model. This forecast is validated for precession and if the precision is within acceptable choice, the Machine Learning algorithm is deployed. If the precision value is not accepted, the Machine Learning algorithm is trained to perform a number of iterations couple of times with an enlarged training data set. Figure 1: Machine learning algorithm [4] The question arises as how to train the machines so that they can be made to think like humans. The answer to this question is provided by artificial neural networks (ANNs).