Classification Of Handwritten Numerals Research Articles

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

Classification of Handwritten Devanagari Number – An analysis of Pattern Recognition Tool using Neural Network and CNN

This paper majorly concerns the classification of handwritten numerals of Devanagari Script. The major contributions in this paper are 1) Development of a dataset for handwritten numerals similar to MNIST dataset, 2) Analysis of Pattern Recognitions tools based on NN and Convolution Neural Network, 3) Detailed discussion on the results by calculating the Precision, recall and F-measure values and compared with the other dataset available online. The present dataset includes 4,282 handwritten numerals of Devanagari which are collected from people of different ages. In the methodology developed in this paper, all the numerals are extracted from the image. After pre-processing, the images are resized to 30X30 which are later converted to vectors. These vectors with labels are the inputs for the classifiers. ANN classifier is designed by using PRTool and Deep Learning network is designed to make comparison with ANN. Data for training and testing splits into different ratios – 80:20, 70:30, 60:40 and 50:50. This research has achieved accuracy of more than 95%. The results of the dataset generated are compared with the dataset available online.

M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification

Abstract The feature selection process is very important in the field of pattern recognition, which selects the informative features so as to reduce the curse of dimensionality, thus improving the overall classification accuracy. In this paper, a new feature selection approach named Memory-Based Histogram-Oriented Multi-objective Genetic Algorithm (M-HMOGA) is introduced to identify the informative feature subset to be used for a pattern classification problem. The proposed M-HMOGA approach is applied to two recently used feature sets, namely Mojette transform and Regional Weighted Run Length features. The experimentations are carried out on Bangla, Devanagari, and Roman numeral datasets, which are the three most popular scripts used in the Indian subcontinent. In-house Bangla and Devanagari script datasets and Competition on Handwritten Digit Recognition (HDRC) 2013 Roman numeral dataset are used for evaluating our model. Moreover, as proof of robustness, we have applied an innovative approach of using different datasets for training and testing. We have used in-house Bangla and Devanagari script datasets for training the model, and the trained model is then tested on Indian Statistical Institute numeral datasets. For Roman numerals, we have used the HDRC 2013 dataset for training and the Modified National Institute of Standards and Technology dataset for testing. Comparison of the results obtained by the proposed model with existing HMOGA and MOGA techniques clearly indicates the superiority of M-HMOGA over both of its ancestors. Moreover, use of K-nearest neighbor as well as multi-layer perceptron as classifiers speaks for the classifier-independent nature of M-HMOGA. The proposed M-HMOGA model uses only about 45–50% of the total feature set in order to achieve around 1% increase when the same datasets are partitioned for training-testing and a 2–3% increase in the classification ability while using only 35–45% features when different datasets are used for training-testing with respect to the situation when all the features are used for classification.

Pattern recognition with a Bayesian kernel combination machine

In this paper, we describe a Bayesian classification method that informatively combines diverse sources of information and multiple feature spaces for multiclass problems. The proposed method is based on recent advances in kernel approaches where the integration of multiple object descriptors, or feature spaces, is achieved via kernel combination. Each kernel constructs a similarity metric between objects in a particular feature space and then having a common metric across modalities an overall combination can be constructed. We follow a hierarchical Bayesian approach, which introduces prior distributions over random variables and we construct a Gibbs sampling Markov chain Monte Carlo (MCMC) solution which is naturally derived from the employed multinomial probit likelihood. The methodology is the basis for possible deterministic approximations such as variational or maximum-a-posteriori estimators, and it is compared against the well-known classifier combination methods on the classification of handwritten numerals. The results of the proposed method show a significant improvement over the best individual classifier and match the performance of the best multiple classifier combination, whilst reducing the computational requirements of combining classifiers and offering additional information on the significance of the contributing sources.

A shape-recognition model using dynamical links

A shape-recognition method is proposed, inspired from the dynamic-link theory of von der Malsburg (1981). The quality of a match between two images is assessed through an elastic cost functional; the minimal value reached by the cost over a suitably-defined space of maps is viewed as a distance between these two images. Experiments on nearest-neighbour classification of handwritten numerals are presented, using a computationally effective procedure for finding a reliable estimate of the matching distance.

A shape-recognition model using dynamical links

A shape-recognition method is proposed, inspired from the dynamic-link theory of von der Malsburg (1981). The quality of a match between two images is assessed through an elastic cost functional; the minimal value reached by the cost over a suitably-defined space of maps is viewed as a distance between these two images. Experiments on nearest-neighbour classification of handwritten numerals are presented, using a computationally effective procedure for finding a reliable estimate of the matching distance.

Experiments on Handwritten Numeral Classification

Results of the application of a new edge-defining preprocessor to the recognition of handwritten numerals are presented. The decision mechanisms which were simulated are simple and are closely related to maximum likelihood decisions. Results are presented of experiments in which 1000 numeral patterns were used to set up a memory, which was then tested on a further 1000 samples. Success rates approaching 95 percent on completely unselected data have been measured. A convenient rejection procedure is described, and the error reject curves produced are shown. These are so encouraging that the scheme must be regarded as the basis of a practical machine for many real life applications.