© IJCIRAS May 2018 | Vol. 1 Issue. 1 IJCIRAS1004 WWW.IJCIRAS.COM 14 A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITION Aditi M Joshi 1 , Ashish D Patel 2 1 Information Technology Department, Gujarat Technological University, Ahmedabad 2 Professor, Computer Engineering Department, Gujarat Technology University, Ahmedabad Abstract The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach. Keyword: deep learning, image classification; Number recognition, Shape Based Recognition, (chain-code Biased) Hub, Hidden Markov Models(HMM), Pattern recognition, Freeman chain code, Image processing, convolutional neural networks, Deep convolutional neural networks, Power Quality. 1.INTRODUCTION Deep learning has become the most popular approach to develop Artificial Intelligence (AI) machines that perceive and understand the world. Here is an overview of deep learning methods for image classification and number recognition in images. Deep Learning uses neural networks that transmit data across multiple processing layers to interpret the properties and relationships of the data. Advanced learning algorithms are largely self- centered in data analysis as they go into production. Researchers have experienced many ups and downs while early work on artificial neural networks has always been of particular interest to researchers. Neural network-based methods have been successfully applied t`o classification, clustering, recognition, approximation, forecasting and problems in medicine, biology, commerce, robotics, and so forth. The latest advances in this area have been made through the invention of advanced learning methods. Hardware and software for parallel computing. For processing numerical data, the human brain is so sophisticated that we recognize objects in a few seconds, without much difficulty. Computer vision is more ambitious. It tries to mimic the human visual system and is often associated with scene understanding. Most image processing algorithms produce results that can serve as the first input for machine vision algorithms. Image processing is a logical extension of signal processing. When an unknown animal is encountered, we try to recognize it by comparing its features (called patterns) with known stored patterns that we already have. This process of comparing an unknown object with stored patterns to recognize the unknown object is called classification. Thus, classification is the process of applying a label or pattern class to unknown instance. In the absence of any prior knowledge of the object or stored pattern, we use a trial and error process to recognize the object. This trial and error process of grouping of objects is called clustering. Most organizations consult handwritten documents such as forms, checks, etc. The manuscript documents are converted and stored in digital formats for easy retrieval. Without a handwritten character recognition software, the source would require the employment of dedicated employees to convert the handwritten document into a digital format by manually entering the text. Today it is very important to store the information available in paper