Online Handwritten Character Recognition (OHCR) is the method of recognizing characters by a machine while the user writes, in which the handheld devices record (x, y) coordinates of the track of the character. With the advent of handheld devices, there is a great attention towards OHCR of regional languages. Preprocessing is the main phase, in OHCR, as it increases the performance of succeeding phases, by removing the inconsistency or the redundancy present in the data collected in real-world environment. In this paper, we depict the model of Preprocessing of Online Handwritten Telugu Strokes. The preprocessing steps we address in our article are Normalization, Smoothing, Duplicate Point Removal, Interpolation, Dehooking and Resampling. Preprocessing data performance is evaluated through parameters namely recognition accuracy, recognition speed, false acceptance rate and false rejection rate over HP labs dataset hpl-Telugu-ISO-char-online-1.0. The dataset contains samples of the 166 character classes collected of different writers on ACECAD Digimemo (A4 sized) using an AcecadDigi memo DCT application. It consists of 270 samples on average for each of 166 Telugu "characters" written by native Telugu writers.

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