Odia Numeral Research Articles

MJCN: Multi-objective Jaya Convolutional Network for handwritten optical character recognition

In recent years, the non-handcrafted feature extraction methods have gained increasing popularity for solving pattern classification tasks due to their inherent ability to extract robust features and handle outliers. However, the design of such features demands a large set of training data. Meta-heuristic optimization schemes can facilitate feature learning even with a small amount of training data. This paper presents a new feature learning mechanism called multi-objective Jaya convolutional network (MJCN) that attempts to learn meaningful features directly from the images. The proposed scheme, unlike the convolutional neural networks, comprises a convolution layer, a multiplication layer, an activation layer and an optimizer known as multi-objective Jaya optimizer (MJO). The convolution layer searches meaningful patterns in an image through the local neighborhood connections and the multiplication layer projects the convolutional response to a more compact feature space. The weights used in these layers are initialized randomly and MJO is then introduced to optimize the weights. The main objective of MJO is to maximize the inter-class distance and minimize the intra-class variance. The feature vectors are finally derived using the optimized weights. The derived features are finally fed to a set of standard classifiers for recognition of characters. The performance of the proposed model is evaluated on various benchmark datasets, namely, NITR Odia handwritten character, ISI Kolkata Odia numeral, ISI Kolkata Bangla numeral, and MNIST as well as a newly developed dataset NITR Bangla numeral. The experimental results show that the proposed scheme outperforms other state-of-the-art approaches in terms of recognition accuracy.

Hidden Markov Model Based Odia Numeral Recognition Using Moment and Structural Features

Optical character recognition (OCR) is a strategy to perceive character from optically checked and digitized pages. OCR plays an important role for Indian script research. The official language of the state Odisha is Odia. OCR face an incredible difficulties to recognize Odia language due to similar shape characters, their complex nature, the complicated way in which they combine form to compound character, use of Matra etc. Each character and numbers are passed through several modules like binarization, noise removal, segmentation, line segmentation, word segmentation, skeletonization, deskewing, thinning, thickening. The input picture is standardized to a size of 50 x 50 2D pictures. HMM is a stochastic process which has utilized in various applications for example speech recognition, Handwriting recognition, Gesture recognition. In this paper we utilized HMM to recognize the Odia character and numbers. Hidden Markov Model have many advantages such as resistant to noise, handle contrast recorded as a hard copy and the HMM devices are effectively accessible. In our proposed method we have developed an efficient recognition algorithm using Hidden Markov model based on moment based and structural feature to recognize Odia characters and numerals.

A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

Optical Character Recognition is one of the most interesting and highly motivated areas of research, which has been very much ap-preciated in different aspect to the area of digitations world. Here in this paper we have suggested a probabilistic approach for develop-ing recognition system for handwritten Odia numerals. To report a good level of recognition of Odia scripts is quite challenging with respect to other Indian scripts .All the procedure are sequentially enclosed to develop an recognition model and report a successful recognition accuracy. Here we have performed the analysis over to standard handwritten numeral database named as IITBBS Odia Numeral Database, which is collected from IIT Bhubaneswar. In the suggestive recognition system we have adopted a 2D-Gabor wavelet transformation approach for selection of feature vector. Apart from it we have also noted down the dimensional reduction to the obtained feature vector by sustaining to PCA. In order to predict high recognition rate we have followed up by RBF Neural Network classifier. In addition to it we have also evaluate different version of RBF like Gaussian and Polynomial. Performing over 400 samples each of 10 categories (400*10) number of Odia numeral images, we have maintained a well-defined training and testing ratio in the clas-sifier and achieved 98.02%, 96.8%.recognition rate for the reported classifiers.

BESAC: Binary External Symmetry Axis Constellation for unconstrained handwritten character recognition

We propose a novel perception driven feature extraction called binary external symmetry axis constellation (BESAC) and a fast Boolean matching character recognition technique. A constellation model using a set of external symmetry axes which are perceptually significant can uniquely represent a handwritten character pattern. This model generates two histograms of orientations that are binary coded and concatenated to produce the proposed BESAC feature. A two stage classification strategy is adopted where a parallel Hamming Distance dissimilarity matching is performed on the extracted BESAC feature to achieve fast recognition along with perceptual closure part detection on look-alike characters. We adopt a 10-fold cross validation strategy to evaluate the performance of our algorithm on two major Indian languages, Bangla and Odia with four benchmark databases (ISI Kolkata Bangla numeral, ISI Kolkata Odia and IITBBS Odia numeral, and a newly created IITBBS Odia character database). The average time for classifying an unknown handwritten character is reported to be significantly less than the existing methods. The average recognition accuracy using the proposed approach is proved to outperform the state-of-the-art accuracy results on ISI Kolkata Odia numeral database (99.35%), IITBBS Odia numeral (98.9%), ISI Kolkata Bangla numeral database (99.48%) and IITBBS Odia character (95.01%) database.

Model based odia numeral recognition using fuzzy aggregated features

In this paper, an efficient scheme for recognition of handwritten Odia numerals using hidden markov model (HMM) has been proposed. Three different feature vectors for each of the numeral is generated through a polygonal approximation of object contour. Subsequently, aggregated feature vector for each numeral is derived from these three primary feature vectors using a fuzzy inference system. The final feature vector is divided into three levels and interpreted as three different states for HMM. Ten different three-state ergodic hidden markov models (HMMs) are thus constructed corresponding to ten numeral classes and parameters are calculated from these models. For the recognition of a probe numeral, its log-likelihood against these models are computed to decide its class label. The proposed scheme is implemented on a dataset of 2500 handwritten samples and a recognition accuracy of 96.3% has been achieved. The scheme is compared with other competent schemes.