An Indoor Sign Dataset (ISD): An Overview and Baseline Evaluation Jo˜ ao L. R. Almeida, Franklin C. Flores, Max N. Roecker, Marco A. K. Braga and Yandre M. G. Costa Departament of Informatics, State University of Maring´ a, Maring´ a, Paran´ a, Brazil Keywords: Indoor signs, Visually Impairment, Indoor Signs Dataset, Convolutional Neural Networks. Abstract: Visually impaired people need help from others when they need to find specific destinations and cannot guide themselves in indoor environments using signs. Computer Vision Systems can help them with this kind of tasks. In this paper, we present to the research community an Indoor Sign Dataset (ISD), a novel dataset composed of 1,200 samples of indoor signs images labeled into one of the following classes: accessibility, emergency exit, men’s toilets, women’s toilets, wifi and no smoking. The ISD dataset consists of images in different environments conditions, perspectives, and appearance that turns the recognition task quite challen- ging. A data augmentation technique was applied, generating 69,120 images. We also present baseline results obtained using handcrafted features, like LBP, Color Histogram, HOG, and DAISY applied on SVM, k-NN, and MLP classifiers. We further make non-handcrafted features learned using convolutional neural networks (CNN). The best result was obtained using a CNN model, with an accuracy of 90.33%. This dataset and techniques can be applied to design a wearable device able to help visually impaired people. 1 INTRODUCTION Approximately of 285 million people have some vi- sual impairment in the world, of whom 39 million are completely blind (Prajapati and Shah, 2016). Visual impairment is a severe condition and can turn daily tasks into challenging ones. Indoor signs are marks with symbols and/or text that communicates essen- tial social rules of the environment: whether it is to display information, to call attention or even to show local prohibitions (Wang et al., 2013). In public pla- ces, some typical examples of indoor signs are men’s and women’s toilet signs, guiding signs to exit door or stairs ahead and signs to inform local wifi con- nection. Visually impaired people are not able to re- ceive this information and they need to use some spe- cialized equipment to help them to move and to inte- ract with the environment. In the last years, the de- creasing costs of hardware and the raising attention of the computer vision research field, pattern recogni- tion, and machine learning brought new perspectives on how the technology can help visually impaired pe- ople. Image object recognition, in the last years, has been gaining attention in the computer vision rese- arch field. Nowadays, there is a wide range of datasets in different object recognition problems which inclu- des human face, vehicle, food, alphanumeric charac- ter and transit signs. These datasets have the main goal to present themselves as a benchmark to com- pare different techniques and methods in the specific problem. With the rising awareness to research in the field of autonomous vehicles, the number of datasets of recognition problems regarding public and traffic signs has significantly increased in the last years. Despite the many datasets available, only a few of them address the indoor environment sign recogni- tion problem, and many of them are in early stage of development (Ni et al., 2014). One can find in the li- terature some works presenting the use of technology to aid visually impaired people to recognize indoor signs (Ni et al., 2014) (Wang et al., 2013), (Kunene et al., 2016). The recognition of indoor signs is not analogous to the recognition of traffic signs for two significant con- cerns: the highlight from the background, and appea- rance standardization (Ni et al., 2014). Traffic signs are heavily highlighted in the background and usu- ally located in higher spots with good sight. Traffic signs also typically have a standardized appearance, with low or no difference in the dimensions, form and color. In opposition, indoor signs are often located Almeida, J., Flores, F., Roecker, M., Braga, M. and Costa, Y. An Indoor Sign Dataset (ISD): An Overview and Baseline Evaluation. DOI: 10.5220/0007375705050512 In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), pages 505-512 ISBN: 978-989-758-354-4 Copyright c  2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 505