Abstract This paper focuses on using the augmented reality (AR) technology to create visual-aids through display for early childhood learning. The proposed methodology works on the principle of augmenting 3D virtual objects over the English alphabets that are used as printed markers. The important steps of a typical marker-based AR application are, (i) detection of markers in the field of view (FOV) of the camera, (ii) identification of the marker, (iii) estimating the pose of the marker, and (iv) rendering 3D virtual content over the marker in a live video stream. We have formulated the marker identification process as a classification problem which has been accomplished with the help of convolutional neural networks (CNN). The effectiveness of the marker identification process using CNN is validated by comparing its identification accuracy with support vector machine (SVM) classifier. The marker identification by the CNN model shows better accuracy than SVM. After successful marker identification and pose estimation, virtual objects are rendered over the 2D projection of the alphabets. The seamless augmentation of the virtual objects over the markers are rendered on display. The setup has been tested on a large dataset and it is believed to create engaging experience for the kids, especially the kindergarten age group.

中文翻译：

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使用卷积神经网络架构为儿童设计基于标记的增强现实学习环境

摘要 本文侧重于使用增强现实 (AR) 技术通过显示来创建用于幼儿学习的视觉辅助工具。所提出的方法基于在用作印刷标记的英文字母上增强 3D 虚拟对象的原则。典型的基于标记的 AR 应用的重要步骤是，(i) 在相机的视野 (FOV) 中检测标记，(ii) 识别标记，(iii) 估计标记的姿态，以及(iv) 在实时视频流中的标记上渲染 3D 虚拟内容。我们已将标记识别过程制定为分类问题，该问题已在卷积神经网络 (CNN) 的帮助下完成。通过将其识别精度与支持向量机 (SVM) 分类器进行比较，验证了使用 CNN 的标记识别过程的有效性。CNN 模型的标记识别显示出比 SVM 更好的准确性。在成功的标记识别和姿态估计之后，虚拟对象被渲染到字母表的 2D 投影上。虚拟对象在标记上的无缝增强呈现在显示器上。该设置已经在大型数据集上进行了测试，相信它可以为孩子们创造引人入胜的体验，尤其是幼儿园年龄组。虚拟对象在标记上的无缝增强呈现在显示器上。该设置已经在大型数据集上进行了测试，相信它可以为孩子们创造引人入胜的体验，尤其是幼儿园年龄段的孩子。虚拟对象在标记上的无缝增强呈现在显示器上。该设置已经在大型数据集上进行了测试，相信它可以为孩子们创造引人入胜的体验，尤其是幼儿园年龄段的孩子。