Handwriting Character Recognision by using Fuzzy Logic

This paper describes a technique that can be used to generate fuzzy rules to extract the features of handwritten characters. The feature extraction is complicated problem as different people write the same character in different ways. The development of a technique that can generate the description of handwritten characters is still a challenge for the handwritten recognition systems. The fuzzy logic offers a good opportunity to build rule-based feature extraction technique for handwritten characters with low computational cost

Document Analysis and Recognition, …, 1995

Recognition methods use different features to assign a pattern to a prototype class. The recognition accuracy strongly depends on the selected features. In this paper we present a novel fuzzy methodology to extract appropriate fuzzy features from the handwriting data. From these meaningful features a set of linguistic rules are derived which in turn constitute a fuzzy rule base for character recognition. The fuzzy features are confined to their meaningfulness with the help of multistage feature aggregation scheme.

Pattern Recognition, 2003

This paper presents an innovative approach called box method for feature extraction for the recognition of handwritten characters. In this method, the binary image of the character is partitioned into a ÿxed number of subimages called boxes. The features consist of vector distance () from each box to a ÿxed point. To ÿnd the vector distances of all the pixels, lying in a particular box, from the ÿxed point are calculated and added up and normalized by the number of pixels within that box. Here, both neural networks and fuzzy logic techniques are used for recognition and recognition rates are found to be around 97 percent using neural networks and 98 percent using fuzzy logic. The methods are independent of font, size and with minor changes in preprocessing, it can be adopted for any language.

2008

In this paper we present a new method to extract the elementary perceptual codes from on-line handwriting scripts. This approach uses the advantages of fuzzy sets theory to classify the on-line handwriting strokes into elementary perceptual codes and the Betaelliptic model for the generation of complex handwriting movements. Human perceptual system is based on some basic features in order to read, write and recognize handwriting. These features are the elementary perceptual codes (EPC) which are on the base to identify patterns. The writing process implies the use of many constraints including the psychological and the physiological ones of the writer. This new method has been tested on the developed data base containing characters, digits and Arabic texts. The achieved results show successful representations of handwritten script with EPCs. Good recognition rates are also reached where the elementary perceptual codes are applied as the characteristics vector of an on-line handwriting Arabic recognition system.

2020

This paper presents a state of the art supervised fuzzy pattern recognition system for recognition of Assamese handwritten characters. The fuzzy classifier is well suited for applications with ambiguities and handwritten character recognition is such a task. The dataset used in this experiment is taken from ISI Kolkata. After preprocessing images are normalized into uniform size 42x32 and then two features namely distance vector and density vector have been extracted. The experiment has two stages, training and testing. In first stage we extract distance vector and density features from uniform zones of the binary images for training classes and estimate the mean and variance for each class. In second stage we use this mean and variance to calculate the membership values for each unknown character of the testing set of data. An exponential fuzzy membership function is used for this purpose. Finally we recognize an unknown test character as that class for which it gives highest membe...

IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04., 2004

This paper presents an off-line writer-independent handwriting analysis system which utilizes both classical crisp and fuzzy methodologies to output possible personality traits of the writer. The design deploys an analytical handwriting analysis approach based on two primitives, the baseline and the slant angle of the characters. The objective of the design strategy is to present a group of parameters for handwriting analysis based on the text. These parameters allow for the classification of writing into different categories which could be used as a preliminary step for outputting the personality traits of the writer. Two parameters, the baseline and the slant-angle, are the inputs to a rule-base which outputs the personality trait category. The evaluation of the baseline is uon-fnzzy (crisp) whereas the evaluation of the slant-angle utilizes the fuzzy paradigm. The approach is based on a combination of classical geometric arithmetic evaluation and fuzzy control designs. For determination of the base line angle two methodologies are explored: the geometric-features based segmentation method and B method based on biologically inspired generation theories or the low pass fdtering method. We utilize the geometric features evaluation for the baseline extraction since it proves more robust with respect to the variations of the handwriting in an off-line environment. For determination of the slant type a fuzzy technique is adopted to determine the contributions of the slant-type angle to each of the five variations of the slant-type categories. The uncertainties in the system model are expressed by fuzzyvalued model parameters with their membership functions derived from experimental data. In total five variations of slant type are considered. These include extreme left, controlled left, vertical, controlled right and extreme right. Fifteen personality traits PTl-PT15 were identified and sets of rules formulation were created, (e.g., IfInputl is "level" and "Input2" is "Controlled Left" then Output is PTx.) The proposed approach takes advantage of two differing methodologies that have clear outputs to evaluate two attributes of handwriting. The outputs are utilized to determine a personality trait The system can he further enbaneed by including more parameters such as size of letters, spacing between letters and other attributes of bandwriting as part of the inputs for trait determination.

2020

This paper presents a novel system for classification and recognition of handwritten Hindi script using fuzzy rule based approach. Classification & recognition of handwritten Hindi script is a complex task as characters are cursive in nature and demonstrate a lot of similar features. The quality of fuzzy logic to deal with vague and imprecise data makes it appropriate for such problems. In this paper, we focus on two or three letter words without modifiers. Prior to recognition, handwritten words are preprocessed and segmented into individual characters. The performance of an optical character recognition system extremely depends on the procedure used to extract quality features from characters. During classification stage characters are classified into seven classes using fuzzy if-then rules based on one of the most important component of Hindi characters -the vertical bar. Features such as curves, lines, junction points and endpoints are used at the recognition stage. A 3x3 mask i...

SSRN Electronic Journal, 2000

In the present paper, we have given a method to recognize a handwritten character by using Fuzzy membership function. Ten sample images of each character in matrix form are fused together to make one standard matrix of the character. The unknown character to be tested for identification is also converted to an image matrix and compared with each standard matrix and thereby recognized by using the Fuzzy scores generated. Several binary images have been tested to demonstrate the effectiveness of the system. This method improves the character recognition method of Chakraborty and Sil (2005) in terms of accuracy.

This is an overview of the most recent published approaches to solving the handwriting recognition problem. This paper is aimed to clarify the role of handwriting recognition in accordance with technologies nowadays, such as Similarity Based for Handwriting Recognition, Fuzzy based handwriting recognition, Neural Network Based Handwriting Recognition, Binary Segmentation Based Handwriting Recognition. Similarity Based for Handwriting Recognition is aimed to to create the set of isolated character images used as training set for the writer-dependent handwriting recognizer. Fuzzy based handwriting recognition is used for handwriting recognition, it is also as preprocessing step. Neural Network Based Handwriting Recognition, both online and offline handwriting recognition can be done by using neural network. Neural network method proposed are Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Bidirectional Neural Networks (BNNs), Connectionist Temporal Classification (CTC), Multidimensional Recurrent Neural Networks and Hierarchical Subsampling Recurrent Neural Network. Binary Segmentation Based Handwriting Recognition, basic idea is find and divide a lexicon into two sub images and selection sub images is repeated until terminating condition is reached. This paper review aiming to find which is the best method to solve handwriting recognition problem. Keyword : handwriting recognition, neural network, Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Bidirectional Neural Networks (BNNs), Connectionist Temporal Classification (CTC), Binary segmentation algorithm (BSA).

Character Recognition, 2010