Approach For Handwritten Character Recognition Research Articles

ML & DL approach for Handwritten Character Recognition for Gujarati Characters

ML & DL approach for Handwritten Character Recognition for Gujarati Characters

A ranking-based feature selection approach for handwritten character recognition

Feature selection is generally considered a very important step in any pattern recognition process. Its aim is that of reducing the computational cost of the classification task, in an attempt to increase, or not to reduce, the classification performance. In the framework of handwriting recognition, the large variability of the handwriting of different writers makes the selection of appropriate feature sets even more complex and have been widely investigated. Although promising, the results achieved so far present several limitations, that include, among others, the computational complexity, the dependence on the adopted classifiers and the difficulty in evaluating the interactions among features. In this study, we tried to overcome some of the above drawbacks by adopting a feature-ranking-based technique: we considered different univariate measures to produce a feature ranking and we proposed a greedy search approach for choosing the feature subset able to maximize the classification results. In the experiments, we considered one of the most effective and widely used set of features in handwriting recognition to verify whether our approach allows us to obtain good classification results by selecting a reduced set of features. The experimental results, obtained by using standard real word databases of handwritten characters, confirmed the effectiveness of our proposal.

Probabilistic Neural Network based Approach for Handwritten Character Recognition

In this paper, recognition system for totally unconstrained handwritten characters for south Indian language of Kannada is proposed. The proposed feature extraction technique is based on Fourier Transform and well known Principal Component Analysis (PCA). The system trains the appropriate frequency band images followed by PCA feature extraction scheme. For subsequent classification technique, Probabilistic Neural Network (PNN) is used. The proposed system is tested on large database containing Kannada characters and also tested on standard COIL-20 object database and the results were found to be better compared to standard techniques.

Handwritten character recognition using low resolutions

This paper deals with an experimental study into the recognition of handwritten characters using representations of low resolution. For this experiment several methodologies were used, such as an attributed graph OCR method, a back-propagation neural network (BNN), a recurrent neural network (RNN), and the Castor neural network (CNN). The attributed graph methodology is based on the representation (mapping) of the text characters onto a small size two-dimensional array of 12×9 cells. For the recognition process each character is considered as a composition of “main” and “secondary” features. The main features are the important parts of a character for its successful recognition. The secondary (or artistic) features are the parts of a character that contribute to its various representations. The attribute graph methodology presented in this paper attempts to prove that the recognition of a reduced-size character provides a robust approach for the recognition of handwritten text. The BNN approach is used here as a comparative recognition method, although it has some serious weaknesses at low resolution. The RNN approach for handwritten character recognition is based upon recurrent neural networks, which have a feedback mechanism. The feedback mechanism acts to integrate new values of feature vectors with their predecessors. The output is supervised according to a target function. These networks can deal with inputs and outputs that are explicit functions of time. A new way of associating shape information was used, which gives very consistent results for handwritten character recognition. In this scheme the “shadow” of each character was considered, to find the distances between the margins of the character. The distances are normalized with respect to the maximum distance in the entire shape to minimize the effect of disproportionally formed characters. In addition, the performance of Castor’s neural net was evaluated for the recognition of handwritten text characters, by using character data sets with various resolutions. For this effort the three neural networks and attributed graph approaches used a set of 5000 handwritten characters, and their results are compared.

Handwritten digit recognition using combined ID3-derived fuzzy rules and Markov chains

In this paper, we present an approach for handwritten digit recognition based on combined ID3-derived fuzzy rules and Markov chains. Both techniques use statistical models on the structural representation of digit images. Skeleton images are adopted to produce fuzzy rules based on the ID3 approach, and contour images are used for the Markov chain based approach. Decision trees produced from the ID3 algorithm are converted to a set of simplified rules which are then fuzzified into a set of fuzzy rules. To retain the classification performance, a two-layer perceptron is applied to optimize defuzzification parameters. On the other hand, a digit contour is traversed in a well-defined order and the Markov chain is used to perform sequential analysis and to match with models. The two classifiers are then combined to complement each other using a three-layer perceptron. The combined classifier achieves a good classification performance and at the same time it overcomes the difficulties in a conventional syntactic approach for handwritten character recognition, such as the scaling problem and lack of machine learning ability. Experimental results on NIST Special Database 3 show that the combined classifier has a significantly improved performance in terms of substitution versus rejection rates. After about 15% of digits that cannot be classified with high confidence by the combined classifier are re-classified by a nearest neighbor classifier using optimized prototypes, the overall classification rate can be as high as 98.6% without rejection.