VOLUME 12 SPECIAL ISSUE 2, JUNE 2021 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info 3 rd International Virtual Conference on Advances in Computing & Information Technology (IACIT-2021) Date: 17-18 May 2021 Organized by School of Computing and Information Technology Reva University, Bengaluru, India © 2020-2022, IJARCS All Rights Reserved 49 ISSN No. 0976-5697 Recognizing Handwritten Characters Using OCR & Converting into TTS Yerrabolu Sailendra Chakravarthy Reddy School of C&IT Reva University Bangalore, India r15cs449@cit.reva.edu.in Rohit Singh School of C & IT Reva University Bangalore, India r14cs274@cit.reva.edu.in Manju More E School of C & IT Reva University Bangalore, India manjumore.e@reva.edu.in Abstract: The aim of the project is "To make Neural Networks aware of handwritten characters", that is, to create a platform that converts handwriting into digital text using Neural Network & Optical Character Recognition. This paper provides an in-depth study of text acquisition, tracking and image recognition with three major contributions. First, it is proposed that a standard framework for the release of image text that equally describes the discovery, tracking, recognition and their relationships and interactions. Second, within this framework, the various methods, systems, and procedures for visualizing the text of an image are summarized, compared, analyzed and the extracted text is converted and extracted by voice. Thirdly, related applications, outstanding challenges, and future directions for image editing are also well discussed. Keywords: Handwritten Characters, Neural Network, Optical Character Recognition, OCR, TTS, OpenCV I. INTRODUCTION (HEADING 1) The aim of the project is to "Train Neural Network to Identify Handwritten Character". According to this Artificial Intelligence is one of the fastest growing branches of computer science in which the neural network plays its part. By using this program, one could translate handwritten English words, numbers and special characters into digital text and save them. Neural Network is a branch of artificial intelligence inspired from the human system which is very complex and very fast. Using this concept, neurons are replaced by nodes and dendroids. Neural Network is unencrypted and unstructured but training. The more he trains, the stronger he becomes. Neural Network helps identify patterns of characters and extracts them by pointing. The project also uses image processing techniques that help to improve the image and convert it into a gray scale and then converted into a binary image of recognition. Before using the neural network, it must be trained using the MNIST database of all others because it has a large amount of data. With the training of this data, neural networks try to understand a given image and visualize it clearly. In our project, we wanted the device to be able to retrieve text from any complex background and read it carefully. Promoted the method used by applications such as "Cam Scanner", assuming that in any complex background, text will be boxed e.g. we think this is a required region that contains text. This is done using warping and cutting. A new image is found on the edge and a border is drawn above the letters. This gives it more meaning. The image is then processed by Optical Character Recognition and Text to Speech to provide audio output. According to the references we have gathered Aisha Sharaf [8] aims at improving the existing handwritten text recognition using machine learning. Machine learning to say is an interesting yet little complicated branch of the computer science. In this paper they used convolution neural network. The neural network is capable of handling complicated data with ease. In general, the data which is handwritten text image is passed through different hidden layers between input and output layer forming a complex network which is correctly connected to the last layer that is output layer. But when coming to image recognition we could use convolution networks instead of general network in a view of computers. Convolution neural networks well in compared to general neural network. Because each pixel increasing in image, increases the parameters exponentially and J. Pradeep [9] mostly discuss about the method of the neural network recognition. The method is multi-layered Feed Forward method. This is a simple neural network method, which is unidirectional that follows a forward direction from input to output. This paper also discusses about a new method for feature extraction that is diagonal feature extraction method. In this paper they used fifty datasets written 26 English alphabet characters. These English alphabets are almost written in 570 different styles. After the project is implemented, it can be used in many other sectors like banking, financing & digitalizing the hand written documents. We can be able to preserve the old documents even in this digital era. II. LITERATURE REVIEW Yingying Zhu [1] suggested that the document, as one of mankind's greatest inventions, played an important role in human life, dating back to antiquity. The rich and accurate information embedded in the text is very useful for many types