Embedded Prototype Subspace Classification : A subspace learning framework

• Handwritten text recognition is a daunting task, due to complex characteristics of handwritten letters. Deep learning based methods have achieved significant advances in recognizing challenging handwritten texts because of its ability to learn and accurately classify intricate patterns. However, there are some limitations of deep learning, such as lack of well-defined mathematical model, black-box learning mechanism, etc., which pose challenges. This paper aims at going beyond the blackbox learning and proposes a novel learning framework called as Embedded Prototype Subspace Classification, that is based on the well-known subspace method, to recognise handwritten letters in a fast and efficient manner. The effectiveness of the proposed framework is empirically evaluated on popular datasets using standard evaluation measures.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Människa-datorinteraktion (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Human Computer Interaction (hsv//eng)

Nyckelord

Handwritten text

Subspaces

Deep learning

t-SNE

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