Vol 6, No 3 (2015) > Electrical, Electronics and Computer Engineering >

Recognizing Offline Handwritten Mathematical Expressions (ME) based on a Predictive Approach of Segmentation using K-NN Classification

Sachin Naik, Pravin Metkewar



Recognition of handwritten mathematical expressions
has been an important topic for many researchers for decades. It remains one of
the most challenging and exciting areas in pattern recognition. In the
recognition process of offline handwritten mathematical expressions,
segmentation is the most important process. Problems in ambiguities of
identifying superscript and subscript in complex offline mathematical
expressions remain one of the most important problem. To the best of our
knowledge little work has been done in the segmentation of offline handwritten
mathematical expressions with respect to superscript and subscript. In this
paper an efficient segmentation technique for superscript, subscript and main
characters within offline handwritten mathematical expressions has been
proposed. This technique is based on the generation of predictions for
superscript, subscript and main characters within handwritten mathematical
expressions, which helps for the reconstruction of mathematical expressions
during the recognition process with their spatial interrelationship. The
proposed system was conducted as an experiment with a database of 300 samples
of scanned mathematical expressions that comprised 2,000 symbols out of which
there were 31 different types of Mathematical Symbols. The classification of
the elements was carried out by the K-NN-classifier based on density features.
This experiment shows remarkable results.

Keywords: Features extraction; K-NN classification; Mathematical Expressions (ME) Recognition; Segmentation

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