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Title: Mem2Speech: An Intelligent Character Recognition-to-Speech Application Using Long Short-Term Memory Networks
Authors: Gonzalvo, Jerome Patrick
Keywords: Artificial Intelligence
Machine Learning
Deep Learning
Recurrent Neural Networks
Long Short-Term Memory Networks
Optical Character Recognition
Intelligent Character Recognition
Issue Date: Jun-2018
Abstract: Smartphones are no longer used solely for communication but also for photography, video recording, internet sur fing, etc. As technology continues to advance, it is possible to apply some techniques to perform tasks such as Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR). Tess2Speech is one of the applications that perform these tasks and it uses the Tesseract OCR Engine. Each trained Tesseract model of Tess2Speech is able to recognize a handwriting style of a user but not that of other users. It is possible for each user to train the engine themselves to t their handwriting but this may be an issue if they don't have the technical background needed to train the engine. This special problem uses Long Short-Term Memory (LSTM) networks for handwritten text recognition. It provides a user-friendly trainer that will help AI experts in training handwriting recognition models. It also provides a mobile application for recognizing handwritten texts using the trained models.
Appears in Collections:Computer Science SP

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