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We propose a framework to extract and binarize handwritten content in lecture videos. The extracted content could potentially be used to index video collections powering content-based search and navigation within lecture videos helping students and educators across the world. A deep learning pipeline is used to detect handwritten text, formulae and sketches and then binarize the extracted content...
We introduce a descriptor for shape feature extraction and matching using keypoints that are extracted from both the foreground and the background of binary images. First, distance transform (DT) is applied on the image after contour detection. Then, connected components (CCs) of pixels having the same intensity are extracted. Keypoints correspond to centers of mass of CCs. A keypoint filtering mechanism...
We present an approach for on-line recognition of handwritten math symbols using adaptations of off-line features and synthetic data generation. We compare the performance of our approach using four different classification methods: AdaBoost. M1 with C4.5 decision trees, Random Forests and Support-Vector Machines with linear and Gaussian kernels. Despite the fact that timing information can be extracted...
Access Math project is a work in progress oriented toward helping visually impaired students in and out of the class-room. The system works with videos from math lectures. For each lecture, videos of the whiteboard content from two different sources are provided. An application for extraction and retrieval of that content is presented. After the content has been indexed, the user can select a portion...
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