Abstract
ORIGIN-Transcoder is a tool that translates the given code from a source language to a destination language and is also known as the end-to-end translator. It assists the software industry by converting the software code from an old or inflexible language to a modern and more flexible language. Studies suggest that developers are seeking transcoders to make their job easier and these transcoders save a lot of time and money. In the existing system, the Transcoders are based on a Rule-Based Algorithm. Since Rule-Based Algorithms require manual correction and expertise in the programming languages, we propose a solution by using a Neural-Based Algorithm. If an employee wants to convert a particular set of code from one language to another, he/she can copy the code and paste it into our software or just take a snap of the code. The code then is extracted by the method of Optical Character Recognition and categorized by Topic Modelling Algorithm. Tokenization is applied to the categorized text to eliminate the punctuation characters. Finally, Recurrent Neural Network gets the desired final code through our Web, Mobile or Desktop Application. Our technique demands less expertise in the targeted programming languages, requires far fewer manual tweaks and is much more efficient than Rule-Based Algorithms.
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