Degraded offline handwritten Gurmukhi character recognition: study of various features and classifiers

Recognition of degraded offline handwritten characters of Gurmukhi script is very challenging task due to the complex structural properties of the script, which is not matter-of-fact in majority of other scripts. A study based on the combination of various feature extraction techniques for character recognition has been presented in this paper. By extracting statistical features in hierarchical order from the pre-segmented degraded offline handwritten Gurmukhi characters, the potential results are analyzed for the recognition. Four types of feature extraction techniques, namely, zoning, diagonal, peak extent based features (horizontally and vertically) and shadow features have been considered in the present study. For classification, three classifiers, specifically, k-NN, decision tree and random forest are employed to demonstrate the effect on the problem of degraded offline handwritten Gurmukhi character recognition. Authors have collected 8960 samples which are partitioned using the partitioning strategy and fivefold cross validation technique. In partitioning strategy, 80% of data is taken as the training dataset and remaining 20% data is considered as the testing dataset. Various parameters for performance measures such as recognition accuracy, false rejection rate (FRR), area under curve (AUC) and root mean square error (RMSE) are also used for analyzing the performance of features and classifiers.