The development of handwriting character recognition (HCR) is an interesting area in pattern recognition. HCR system consists of a number of stages which are preprocessing, feature extraction, classification and followed by the actual recognition. It is generally agreed that one of the main factors influencing performance in HCR is the selection of an appropriate set of features for representing input samples. This paper provides a review of these advances. In a HCR, the set of features plays as main issues, as procedure in choosing the relevant feature that yields minimum classification error. To overcome these issues and maximize classification performance, many techniques have been proposed for reducing the dimensionality of the feature space in which data have to be processed. These techniques, generally denoted as feature reduction, may be divided in two main categories, called feature extraction and feature selection. A large number of research papers and reports have already been published on this topic. In this paper we provide an overview of some of the methods and approach of feature extraction and selection. Throughout this paper, we apply the investigation and analyzation of feature extraction and selection approaches in order to obtain the current trend. Throughout this paper also, the review of metaheuristic harmony search algorithm (HSA) has provide.

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A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

In this paper, we describe hybrid feature extraction for offline handwritten character recognition. The proposed technique is a hybrid of structural, statistical and correlation features. In the first step, the proposed technique identifies the type and location of some elementary strokes in the character. The strokes to be looked for comprise horizontal, vertical, positive slant and negative slant lines–as we observe that the structure of any character can be approximated with the help of a combination of simple straight line strokes. The strokes are identified by correlating different segments of the character with the chosen elementary shapes. These normalized correlation values at different segments of the character give correlation features. For making feature extraction more robust, we add in the second step certain structural/statistical features to the correlation features. The added structural/statistical features are based on projections, profiles, invariant moments, endpoints and junction points. This enhanced, powerful combination of features results in a 157-variable feature vector for each character, which we find adequate enough to uniquely represent and identify each character. Prior, handwritten character recognition problem has not been addressed the way our proposed hybrid feature extraction technique deals with it. The extracted feature vector is used during the training phase for building a support vector machine (SVM) classifier. The trained SVM classifier is subsequently used during the testing phase for classifying unknown characters. Experiments were performed on handwritten digit characters and uppercase alphabets taken from different writers, without any constraint on writing style. The obtained results were compared with some related existing approaches. Owing to the proposed technique, the results obtained show higher efficiency regarding classifier accuracy, memory size and training time as compared to these other existing approaches.

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Handwritten Character Recognition Using Multiclass SVM Classification with Hybrid Feature Extraction

The main challenge in handwritten character recognition involves the development of a method that can generate descriptions of the handwritten objects in a short period of time. Due to its low computational requirement, fuzzy logic is probably the most efficient method available for on-line character recognition. The most tedious task associated with using fuzzy logic for online character recognition is the building of the rule-base that would describe the characters to be recognized. The problem is complicated as different people write the same character in complete different ways. This paper describes a method that can be used to generate a fuzzy value database that describes the characters written by different individuals.

An Efficient Fuzzy Method for Handwritten Character Recognition

Fuzzy logic is a powerful tool to represent imprecise and irregular patterns. This paper presents a novel fuzzy approach for recognizing online Persian (Farsi) handwriting. In this approach, a fuzzy classifier is introduced that uses a combination of the fuzzy LVQ learning model and the expert knowledge. This method applies an FLVQ network to distinguish between the similar tokens that appear at the end of the strokes. For other tokens, fuzzy linguistic terms are used to describe their features. The purposed method was run on a database of Persian isolated handwritten characters and achieved a high recognition rate compared to other available approaches

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A Novel Fuzzy Classifier using Fuzzy LVQ to Recognize Online Persian Handwriting

Fuzzy logic has proved to be a powerful tool to represent imprecise and irregular patterns. This paper presents a novel fuzzy approach for recognizing online Persian (Farsi) handwriting which is also useful for multi-writer environments. In this approach, the representation of handwriting parameters is accomplished by fuzzy linguistic modeling. The representative features are selected to describe the shape of tokens. Fuzzy linguistic terms provide robustness against handwriting variations. The purposed method was run on a database of Persian isolated handwritten characters and achieved a relatively high recognition rate.

A novel fuzzy approach to recognition of online Persian handwriting

Handwritten characters differ from person to person. Thus, when using traditional methods like neural networks and image processing techniques, extensive training of the system is needed. Due to this reason, an attempt was made to develop a system that used the methods that humans use to perceive handwritten characters. Thus, a system that recognizes features in handwritten characters using fuzzy logic was developed. The objectives in the development of this method were: 1. The development of a short and efficient algorithm that tries as much as possible to model human perception. 2. The development of an algorithm that can be implemented on any hardware or software platform through low computational power requirements. A time ordered sequence of coordinates should be sent into the system. The following calculations are then preformed on this set of data: 1. Segmentation of the image based on the direction of movement; 2. Determination of the universe of discourse; 3. Calculation of the local features for each identified segment; 4. Application to fuzzy rule-base for character recognition.

Use of fuzzy feature descriptions to recognize handwritten alphanumeric characters

The main challenge in handwritten character recognition involves the development of a method that can generate descriptions of the handwritten objects in a short period of time. Due to its low computational requirement, fuzzy logic is probably the most efficient method available for on-line character recognition. The most tedious task associated with using fuzzy logic for online character recognition involves the building of the rule-base that would describe the characters to be recognized. This problem is further complicated by the fact that different people write the same character in completely different ways. This paper describes a method that can be used to automatically generate a database containing the fuzzy membership values that describe the characters written by different individuals.

Automatic fuzzy rule base generation for on-line handwritten alphanumeric character recognition

Use of Fuzzy Feature Descriptions to Recognize Handwritten Alphanumeric Characters.

Handwritten characters differ from person to person. Thus, when using traditional methods like neural networks and image processing techniques, extensive training of the system is needed. Because of this, an attempt was made to develop a system that used the methods that humans use to perceive handwritten characters. Thus, a system that recognizes features in handwritten characters using fuzzy logic was developed. The reasons for the selection of fuzzy logic were as follows: 1. Fuzzy logic can be used to model human perception; 2. The mathematics that fuzzy logic requires is extremely fundamental. Thus, any algorithm developed using fuzzy logic would require relatively simple and short calculations; and 3. Due to simplicity of fuzzy calculations, they can be implemented on any hardware or software platform without too much concern for computing power. The objectives in the development of this method were: 1. The development of a short and efficient algorithm that tries as much as possible to model human perception. and 2. The development of an algorithm that can be implemented on any hardware or software platform through low computational power requirements.

G. E. M. D. C. Bandara

Papers

An Efficient Fuzzy Method for Handwritten Character Recognition

Use of fuzzy feature descriptions to recognize handwritten alphanumeric characters

Automatic fuzzy rule base generation for on-line handwritten alphanumeric character recognition

Use of Fuzzy Feature Descriptions to Recognize Handwritten Alphanumeric Characters.

A short method for on-line handwritten character recognition