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Writer identification is one of the areas in pattern recognition that have created a center of attention by many researchers
to work in. Its focal point is in forensics and biometric application as such the writing style can be used as biometric features
for authenticating a writer. Handwriting style is a personal to individual and it is implicitly...
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... discretization process for various images of writers. There are eight columns of ex- tracted invariant feature vectors and the last column is the label of author's class. Eight invariant vectors of feature in one row represent one word image for the writer in the last column. Fig. 2 is continued to perform discretization process as shown in Fig. 3. It is an example to discretize data for writer 1. Discretized feature data of discretization process is shown in Fig. 4 for all data in Fig. ...
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Writer Identification (WI) is one of the areas in pattern recognition that have created a center of attention for many researchers to work in. Recently, its main focus is in forensics and biometric application, e.g. writing style can be used as biometric features for authenticating individuality uniqueness. Existing works in WI concentrate on featu...
Citations
... Applied only a small subset of moment invariant [9] iv. Produce errors if the transformations are subjected to unequal scaling data transformation [10][11][12][13][14][15][16][17][18] v. Data position of pixel is far away from centre coordinate [19] vi. Problem in region, boundary and discrete condition [20] Therefore, more standardize uniform representation of data distributions are needed for recognition of human action. ...
The uniqueness of the human action shape or silhouette can be used for the human action recognition. Acquiring the features of human silhouette to obtained the concept of human action invarianceness have led to an important research in video surveillance domain. This paper discusses the investigation of this concept by extracting individual human action features using integration moment invariant. Experiment result have shown that human action invarianceness are improved with better recognition accuracy. This has verified that the integration method of moment invariant is worth explored in recognition of human action in video surveillance.
... Applied only a small subset of moment invariant [9] iv. Produce errors if the transformations are subjected to unequal scaling data transformation [10][11][12][13][14][15][16][17][18] v. Data position of pixel is far away from centre coordinate [19] vi. Problem in region, boundary and discrete condition [20] Therefore, more standardize uniform representation of data distributions are needed for recognition of human action. ...
The uniqueness of the human action shape or silhouete can be used for the human action recognition. Acquiring the features of human silhouette to obtained the concept of human action invarianceness have led to an important research in video surveillance domain. This paper discusses the investigation of this concept by extracting individual human action features using integration moment invariant. Experiment result have shown that human action invarianceness are improved with better recognition accuracy. This has verified that the integration method of moment invariant is worth explored in recognition of human action in video surveillance.
... The proposed method achieved an 83 % identification rate on a subset of 30 writers from the IAM database. Muda et al. showed that the discretization of features can significantly improve the identification rates [18]. Discretization is performed by exploring the partitioning of features into intervals and unifying the values for each interval. ...
Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.
... There are three steps involved in traditional handwriting identification task, which are pre-processing, feature extraction and classification [20]. Previous studies have explored various methods to enhance traditional task, and improves the classification accuracy. ...
... However, in this experiment, UMI is not used to find the similar shape; instead it is used to find the similar unique features for the same class (writer). In the previous study, data discretization has been used to improve the classifier accuracy [20]. ...
Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification. Various filter and wrapper feature selection methods are selected and their performances are analyzed. This paper describes an improved sequential forward feature selection method besides the exploration of significant features for invarianceness of authorship from global shape features by using various feature selection methods. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship.
... Even though traditional feature selection techniques can be used for acquiring these unique individual significant features [89][90][91][92], it may not be appropriate and feasible. And thus, the traditional handwriting identification framework, which consists of pre-processing, feature extraction and classification [93] is not adequate for this issue. Enhanced WI Framework (EWIF) shown in Fig. 4 [4], consists of feature extraction, feature discretization, and identification has been adopted by [90,92] and produced good result, and therefore it can be concluded that this framework is can be further improved. ...
Feature selection has become the focus of research area for a long time due to immense consumption of high-dimensional data. Originally, the purpose of feature selection is to select the minimally sized subset of features class distribution which is as close as possible to original class distribution. However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. Writer Identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in due to the extensive exchange of paper documents. Its principal point is in forensics and biometric application as such the writing style can be used as bio-metric features for authenticating the identity of a writer. Handwriting style is a personal to individual and it is implicitly represented by unique individual significant features that are hidden in individual's handwriting. These unique features can be used to identify the handwritten authorship accordingly. The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. The key concern in Writer Identification is in acquiring the features reflecting the author of handwriting. Thus, it is an open question whether the extracted features are optimal or near-optimal to identify the author. Therefore, feature extraction and selection of the unique individual significant features are very important in order to identify the writer, moreover to improve the classification accuracy. It relates to invarianceness of authorship where invarianceness between features for intra-class (same writer) is lower than inter-class (different writer). Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. This chapter instead focuses on identifying the unique individual significant features of word shape by using feature selection method prior the identification task. In this chapter, feature selection is explored in order to find the most unique individual significant features which are the unique features of individual's writing. This chapter focuses on the integration of Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique into the proposed hybrid of Writer Identification framework and feature selection framework, namely Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4). Experiments conducted to proof the validity and feasibility of the proposed framework using dataset from IAM Database by comparing the proposed framework to the existing Writer Identification framework and various feature selection techniques and frameworks yield satisfactory results. The results show the proposed framework produces the best result with 99.35% classification accuracy. The promising outcomes are opening the gate to future explorations in Writer Identification domain specifically and other domains generally.
... Among these features are exists the significant individual features which directly unique to those individual. There are three steps involved in traditional handwriting identification framework, which are pre-processing, feature extraction and classification [17]. However, it has been proven that most of preprocessing tasks must be omitted because some of the original and important information are lost, and thus decrease the identification performance in WI [9]. ...
The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. Acquiring these significant features leads to an important research in Writer Identification (WI) domain. This paper is meant to explore the usage of improved discretization method and explore an alternative to Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4) WI framework for Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique in order to find the unique significant features. This paper proposes a novel feature selection framework for handwritten authorship. The promising applicability of the proposed framework has been demonstrated and worth to receive further exploration in identifying the handwritten authorship.
... According to Azah et. al [34], discretization provides loads of advantages when dealing with huge and continues data. The authors have made some comparison on post-discretized and prediscretized data. ...
... Another feature extraction technique based on moment function has been successfully carried out by Azah et. al [34]. The authors have conducted experiment with various words representation to show the effectiveness of the proposed moment function. ...
... Commonly, there are several discretization methods being used, and these include Equal Information Gain (EIG), Maximum Entropy (ME), Equal Interval Width (EIW) and others. Recently, Invariants Discretization has been proposed and successfully given higher identification rates [34]. It is treated as a supervised method that searches for a set of appropriate interval which is entitled to represent writer information's. ...
... Bensefia, Nosary, Paquet and Heutte in [13] mentioned that in traditional handwriting identification task, there are three steps involved, which are pre-processing, feature extraction and classification. Previous studies have explored various methods to enhance traditional task, and improves the classification accuracy. ...
... Previous studies have explored various methods to enhance traditional task, and improves the classification accuracy. One study has been conducted by Muda [13] by incorporating discretization task after feature extraction task, and the results shows significantly improved classification accuracy. This paper extends the work of Muda [13] with a slight modification. ...
... One study has been conducted by Muda [13] by incorporating discretization task after feature extraction task, and the results shows significantly improved classification accuracy. This paper extends the work of Muda [13] with a slight modification. This paper tries to incorporate feature selection after feature extraction task, instead of using discretization task. ...
Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper feature selection methods are selected and their performances are analyzed using image dataset from IAM Handwriting Database. It is also analyzed the number of features selected and the accuracy of these methods, and then evaluated and compared each method on the basis of these measurements. The evaluation identifies the most interesting method to be further explored and adapted in the future works to fully compatible with Writer Identification domain.
... Among these features are exists the significant individual features which directly unique to those individual. There are three steps involved in traditional handwriting identification framework, which are pre-processing, feature extraction and classification [21]. ...
... Even though traditional feature selection techniques can be used for acquiring these significant features, as presented in [3,4,29], it may not be appropriate. And thus, the traditional handwriting identification framework, which consists of pre-processing, feature extraction and classification [21] is not adequate for this issue. An enhanced handwriting identification framework (EHIF), proposed by [20], consists of feature extraction, feature discretization, and identification has been adopted in [4] and produced good result, and therefore it can be concluded that this framework is can be further improved. ...
The uniqueness of shape and style of handwriting can be used to identify the significant features in confirming the author of writing. This paper is meant to propose a novel feature selection framework for Swarm Optimized and Computationally Inexpensive Floating Selection SOCIFS, by exploring existing feature selection frameworks, and compare the performance of proposed feature selection framework against various feature selection methods in Writer Identification in order to find the most significant features. The promising applicability of the proposed framework has been demonstrated in the result and worth to receive further exploration in identifying the handwritten authorship.
... It attracted a lot of interest from and work in several different domains [13], [14], [15]. The discretization method introduced here is based on discretization defined in [16]. ...
The fusion of different forensic modalities for arriving at a decision of whether the evidence can be attributed to a known individual is considered. Since close similarity and high dimensionality can adversely affect the process, a method of score fusion based on discretization is proposed. It is evaluated considering the signatures and fingerprints. Discretization is performed as a filter to find the unique and discriminatory features of each modality in an individual class before their use in matching. Since fingerprints and signatures are not compatible for direct integration, the idea is to convert the features into the same domain. The features are assigned an appropriate matched score, MSbp which are based to their lowest distance. The final scores are then fed to the fusion, FSbp. The top matches with FSbp less than a predefined threshold value, η are expected to have the true identity. Two standard fusion approaches, namely Mean and Min fusion, are used to benchmark the efficiency of proposed method. The results of these experiments show that the proposed approach produces a significant improvement in the forensic identification rate of fingerprint and signature fusion and this findings support its usefulness.
