Background:
In the past decades, handwritten character recognition has received considerable
attention from researchers across the globe because of its wide range of applications in daily life.
From the literature, it has been observed that there is limited study on various handwritten Indian scripts
and Odia is one of them. We revised some of the patents relating to handwritten character recognition.
Methods:
This paper deals with the development of an automatic recognition system for offline handwritten
Odia character recognition. In this case, prior to feature extraction from images, preprocessing
has been done on the character images. For feature extraction, first the gray level co-occurrence matrix
(GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT)
and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are
calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the
feature space and generate more relevant features, principal component analysis (PCA) has been employed.
Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN),
they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN
are separately applied in this study for segregation of character images.
Results:
All the experiments were performed on a system having specification as windows 8, 64-bit operating
system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through
Matlab2014a on a standard database named as NIT Rourkela Odia Database.
Conclusion:
The proposed system has been validated on a standard database. The simulation results
based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy
than the existing methods while requiring least number of features. The recognition rate using RF and
K-NN classifier is found to be 94.6% and 96.4% respectively.
Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography
