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I just made a video presentation of a previously published paper that I hope can be of use for anyone computing image derivatives!

I presented our ongoing work at the "högre seminariet i ekonomisk historia på Stockholms universitet " (4 June 2020). The title was Fast and easy transcription of handwritten documents Abstract Printed books can be converted into searchable machine encoded text using Optical Character Recognition (OCR). However, handwritten text is much harder to convert due to … Continue reading Invited Talk

Please have a look at our video presentation for the WSCG2020 conference. This is the latest research about XAI, which extends on the Embedded Prototype Subspace Classification pipeline. This paper introduces the use of cascading for ensembles with early termination. The idea is to progressively exclude neurons that cannot contribute to the solution. This can … Continue reading Ensembles and Cascading of Embedded Prototype Subspace Classifiers

In order to go beyond the black-box deep neural network, this paper introduces a novel learning framework: Embedded Prototype Subspace Classification (EPSC), which is based on subspaces (or manifolds) for image classification (especially handwritten text). The proposed EPSC framework is intended to be both easy to comprehend and visualise, with mathematically well-defined components. This is … Continue reading Embedded Prototype Subspace Classification: A subspace learning framework

Word spotting use a query word image to find any instances of that word among document images. The obtained list of words is ranked according to similarity to the query word. Ideally, any false positives should only occur in the end of that list. However, in reality they often occur higher up, which decreases the … Continue reading Consensus Ranking for Increasing Mean Average Precision in Keyword Spotting