Artificial Neural Network for Cursive Handwriting Recognition System

A tool that can search over large code corpus directly and list ranked snippets can prove to be an invaluable resource to programmers looking for similar code snippets using natural language queries. It must have a deep understanding of the semantics of source code and queries to evaluate their intent correctly. Over the years, many tools that rely on the textual similarity between source code and query have proven to be ineffective as they fail to learn the high- level semantic understanding of source code and query. While the previous models for code search using deep neural networks do a good job but, most of them only evaluate their models on only a single programming language, mostly Java. In this paper, we propose a novel deep neural network model called Unified Code Net that can handle the intricacies of different programming languages. This model borrows several vital features from different previous models and builds on top of those ideas to make a unified model that can generate document vector embeddings from source code, and using similarity search with the query vector embedding can return the most similar code snippets in any language. This tool can drastically reduce the programmer’s efforts to look for an efficient and viable code snippet for problem at hand which ideally can replace use of search engines for the same. Keywords: semantic code search, natural language processing, information retrievalaCursive Handwriting acknowledgment is an extremely testing zone because of the one of a kind styles of composing starting with one individual then onto the next. Right now, disconnected cursive composing character acknowledgment framework is portrayed utilizing an Artificial Neural Network. The highlights of every character written in the information are extricated and afterward sent to the neural system. Informational collections, having writings of various individuals are utilized in making framework. The suggested acknowledgment framework yields elevated steps of exactness when contrasted with the ordinary methodologies right now. This framework can effectively perceive cursive messages and convert them into auxiliary structure