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International Journal on Document Analysis and Recognition (IJDAR)

Erschienen in:

15.06.2019 | Special Issue Paper

Generalized framework for summarization of fixed-camera lecture videos by detecting and binarizing handwritten content

verfasst von: Bhargava Urala Kota, Kenny Davila, Alexander Stone, Srirangaraj Setlur, Venu Govindaraju

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 3/2019

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Abstract

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 exploit the spatio-temporal structure of our binarized detections to compute associativity information of content across all video frames. This information is later used to segment the video. Experiments are conducted to compare the performance of key components of our framework in isolation, as well as the impact on overall performance, with respect to existing methods. We evaluate our framework on the publicly available AccessMath lecture video dataset obtaining an f-measure of \(94.32\%\) for binary connected components. Code for the framework (including trained weights) and summarization will be released.

Vorheriger Artikel Boosting scene character recognition by learning canonical forms of glyphs

Nächster Artikel Dynamic temporal residual network for sequence modeling

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https://github.com/bhargavaurala/accessmath-ijdar

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Titel: Generalized framework for summarization of fixed-camera lecture videos by detecting and binarizing handwritten content
verfasst von: Bhargava Urala Kota
Kenny Davila
Alexander Stone
Srirangaraj Setlur
Venu Govindaraju
Publikationsdatum: 15.06.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 3/2019
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI: https://doi.org/10.1007/s10032-019-00327-y

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