Mul2MAR combines ARToolKit markers, OpenGL rendering, and red-cyan anaglyph glasses to show virtual objects in apparent 3D on a mobile device, but gives no quantitative validation beyond the author's prior work.
ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images
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abstract
In this work, product tables in invoices are obtained autonomously via a deep learning model, which is named as ExTTNet. Firstly, text is obtained from invoice images using Optical Character Recognition (OCR) techniques. Tesseract OCR engine [37] is used for this process. Afterwards, the number of existing features is increased by using feature extraction methods to increase the accuracy. Labeling process is done according to whether each text obtained as a result of OCR is a table element or not. In this study, a multilayer artificial neural network model is used. The training has been carried out with an Nvidia RTX 3090 graphics card and taken $162$ minutes. As a result of the training, the F1 score is $0.92$.
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Mul2MAR: A Multi-Marker Mobile Augmented Reality Application for Improved Visual Perception
Mul2MAR combines ARToolKit markers, OpenGL rendering, and red-cyan anaglyph glasses to show virtual objects in apparent 3D on a mobile device, but gives no quantitative validation beyond the author's prior work.