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.
In Character Recognition, the Feature extraction has encompassed a well-known role. Here, Feature Extraction centered on Chain code (CC) is implemented. CC encodes every stroke with a string of numbers, in which every number signifies a specific direction wherein the subsequent point on the stroke is present. CC centered feature safeguard information and permits reasonable data to decrease. Disparate CC can signify the same shape since the CC is reliant on starting point. So here, Starting Point and rotation invariant feature extraction technique using Normalized Differential Chain Code (NDCC) is proposed. A two-stage classifier is employed for classification. Here, the NDCC feature is utilized in the pre-classifier and pre-processed (x,y) coordinates are used in the post classifier. In both stages K-NN classifier is used. This feature is verified in HP-Lab data that is present in the UNIPEN format. Investigational outcomes proved that the proposed feature enhances recognition accuracy over the selected dataset.
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