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

*Sachin Naik, Pravin Metkewar*

**Abstract**:

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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