Handwritten Signature Verification

The objective of this paper is to propose an offline signature verification system (OSVS) designed for writer-independent applications. The system shall differentiate between original and forged signatures. The system encompasses key stages such as pre-processing, feature extraction, and model training and testing, employing the Support Vector Machine algorithm for classification. One challenge in creating a good OSVS is to find proper signature features to be used in the training/classification phases. In this research, global and local features are utilized. This includes signature area, mean, standard deviation, perimeter, number of connected components, number of vertical and horizontal edges, number of end points, number of branch points, and number of lines. The contributions of this paper are on several aspects of the offline signature verification process. Investigation in this study include data pre-processing techniques (normalization vs. standardization), kernel selection (Poly vs. RBF), dataset distribution for training and testing (80%-20% vs. 5-fold), and variations of the C and gamma parameters (C=1, 10, 100 and gamma=1, 10, 100). Improving the recognition rate involves removing of features with little or bad effect on the recognition rate. An algorithm for model-agnostic feature importance is executed, revealing that the most crucial features in the classification process are mean, standard deviation, perimeter, number of connected components, and number of end points. Signatures are classified as either original or forgery, and the model's performance is assessed on the CEDAR dataset. Experimental results shows a 95.65%
accuracy of the proposed system when utilizing standardization with the RBF kernel.

Recognizing handwritten text from images is a complex yet essential task in the field of computer vision, especially for document digitization and archival systems. This study introduces a hybrid handwritten text recognition system that integrates a custom-trained deep learning model utilizing Connectionist Temporal Classification (CTC) with advanced preprocessing techniques. The model employs a CNN-BLSTM-CTC architecture capable of learning from variable-length sequences without requiring character-level alignment. To enhance flexibility, the system supports multiple decoding strategies, including Best Path, Beam Search, and Word Beam Search. It is trained and evaluated using the IAM dataset, with performance measured through Character Error Rate (CER) and Word Accuracy metrics. A user-friendly interface enables training, validation, and real-time inference through command-line interaction. The solution also includes optional integration with Google Vision API, expanding recognition capabilities beyond custom model limitations. This work offers a reliable and scalable framework for offline handwritten text recognition, with potential for deployment in educational, governmental, and industrial sectors.

We address, in this work, a new feature generation method for two different approaches of off-line handwritten signature verification (HSV), writer-dependent and writerindependent HSV. The proposed method uses conjointly the contourlet transform and the co-occurence matrix. The contourlet transform allows capturing contour segment directions of the handwritten signature, while the cooccurrence matrix allows describing the number of directions. Experiments are conducted on the well known CEDAR dataset and the classification through the support vector machines (SVM). The obtained results show the effective use of the Contourlet transform for handwritten signature verification comparatively to the state of the art.

We address, in this work, a new feature generation method for two different approaches of off-line handwritten signature verification (HSV), writer-dependent and writerindependent HSV. The proposed method uses conjointly the contourlet transform and the co-occurence matrix. The contourlet transform allows capturing contour segment directions of the handwritten signature, while the cooccurrence matrix allows describing the number of directions. Experiments are conducted on the well known CEDAR dataset and the classification through the support vector machines (SVM). The obtained results show the effective use of the Contourlet transform for handwritten signature verification comparatively to the state of the art.

Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and treatable. Early detection of strokes and their rapid intervention play an important role in reducing the burden of disease and improving clinical outcomes. In recent years, machine learning methods have attracted a lot of attention as they can be used to detect strokes. The aim of this study is to identify reliable methods, algorithms, and features that help medical professionals make informed decisions about stroke treatment and prevention. To achieve this goal, we have developed an early stroke detection system based on CT images of the brain coupled with a genetic algorithm and a bidirectional long short-term Memory (BiLSTM) to detect strokes at a very early stage. For image classification, a genetic approach based on neural networks is used to select the most relevant features for classification. The BiLSTM model is then fed with these features. Cross-validation was used to evaluate the accuracy of the diagnostic system, precision, recall, F1 score, ROC (Receiver Operating Characteristic Curve), and AUC (Area Under The Curve). All of these metrics were used to determine the system's overall effectiveness. The proposed diagnostic system achieved an accuracy of 96.5%. We also compared the performance of the proposed model with Logistic Regression, Decision Trees, Random Forests, Naive Bayes, and Support Vector Machines. With the proposed diagnosis system, physicians can make an informed decision about stroke.

The Autism Spectrum Disorder (ASD) is a neurological disease, which affects the mental, social and physical state of a person. A person of any age group can be found infected by it. It is very difficult to identify, if a person is the victim of this disorder. Classical approaches that find the occurrence of autism in a person is time consuming and expensive. Machine learning approaches, on the other hand, have paved the way for intelligent diagnostics. This paper focusses on identification of specific traits that helps to automate the diagnosis process and further evaluate and perform a comparative analysis of various machine learning algorithms namely K-Nearest Neighbour, Logistic Regression, SVM and Naïve Bayes, to predict the occurrence of autism disorder. Experimental analysis shows that the Naïve Bayes algorithm provides a better accuracy of 99.6% compared to other algorithms.

This review discusses the developments in handwriting recognition and optical character recognition (OCR) systems, with a focus on the transformative role of artificial intelligence (AI) in enhancing accuracy, precision, and scalability. Modern methods include convolutional neural networks (CNNs), recurrent neural networks (RNNs), hybrid CNN-RNN frameworks, and transformer-based architectures. These methods have been evaluated to address a wide range of tasks including handwritten text recognition (HTR), signature verification (SV), and multilingual text analysis. The impressive results have been achieved across a variety of datasets with 99.98%-digit recognition and more than 97% online handwritten word recognition. This involves high-end models such as hybrids CNN-RNN and transformer architecture, which have shown their effectiveness in capturing spatial and temporal features and adapting to handwriting variability in writing and linguistic contexts. The review underlines the importance of texture and structural feature analysis to progress with AIbased handwriting systems while emphasizing the extraction of fine-grained details such as pen pressure and stroke dynamics. Despite these advances, the issue of managing handwriting variability, dynamic styles, and underrepresented scripts like non-Latin languages still persists. Some open areas of research include robustness improvement, fine-tuning models to better adapt to variability, and interdisciplinary applications in forensic analysis, education, and medical diagnostics.

This study explores the distinction between Fully Connected (FC) and Dense layers in neural network-based image processing. Traditional FC layers from early deep learning models connect every neuron to all previous layer neurons, leading to high computational demands and increased risk of overfitting. Dense layers, a more refined form of FC layers, help lower parameter counts, enhancing efficiency in handling large-scale image data. The research examines how each layer type influences image classification performance metrics. Findings show Dense layers offer faster training and improved efficiency compared to general FC layers. However, FC layers often yield better outcomes in complex tasks requiring strong model expressiveness. The study highlights the trade-off between speed and accuracy in image-based deep learning, offering valuable insights for experienced developers.

An integral step in the development, expression, and support of my forensic opinion is researching the recognized and authoritative writings that would explain the causes of the significant characteristics observed during my forensic examination of the evidence. Herein is a selection of excerpts resulting from my exploration regarding "habit/habitual" in the literature that I reviewed for my commission on a case involving the investigation of a suspicious situation.

to cation and Analysis (SIGVA 1.0), Usable With IBM-PC Compatible Machines by Maan Ammar, Mohammed Aita, Basil Younaki and Bassam Takwa at Basic and Applied ISSues in Handwriting and Drawing Research Conference. University of Western Ontario London, Ontario, Canada Aug. 6-10, 1995. International Journal of pattern Recognition and Artificial Intelligence “Progress in Verification of Skillfully Simulated Handwritten Signatures' Maan Ammar. World Scientific Publ. Co., 1991, vol. 5, No. 1 & 2, pp. 337–351.

Classification of traditional handwriting script and to preserve many cultures have been developed in some parts of the world, including image classification of handwriting Kaganga script. This study aims to propose a new combination model by implementing top-hat transform (THT) and contrast-limited adaptive histogram equalization (CLAHE) with discrete wavelet transform (DWT) to support the performance of the convolutional neural network (CNN) in Kaganga script classification. The top-hat transform and contrast-limited adaptive histogram equalization with discrete wavelet transform Fusion L2 convolutional neural network (DWT-THCL L2 CNN) models get the best accuracy from the CNN with L1 regularization, CNN with dropout regularization, CNN with L2 regularization and CNN with L2 regularization and CLAHE models. Based on the experimental results, the DWT-THCL L2 CNN model successfully increased training accuracy by 7.76%, validation accuracy by 5.11%, and testing accuracy by 3.73% from the CNN L1 model. The DWT-THCL L2 CNN model received a training accuracy of 99.87%, validation accuracy of 82.61%, and testing accuracy of 82.61%, while the CNN model with L1 regularization (L1 CNN) only received a training accuracy of 92.11%, validation accuracy of 77.50%, and testing accuracy of 78.88%.

Background: Fluorescence Microscope is a promising analytical method for the examination of the sequence of strokes of writing inks of different colours, brands, and models in a questioned document. Aims and Objectives: In the present article, an attempt has been made to categorize the writing inks of different brands and colours on the basis of their fluorescence. Materials and Methods: A total of 36 writing inks were used to make the different intersections on a standard A4 sheet which were then analyzed under the excitation filters of the Fluorescence Microscope. Results: The results indicated that the different inks can be characterized optically based on the luminescent components of the ink present in crossed lined intersections. Conclusions: Fluorescence Microscopy is a relatively simple, fast, inexpensive, and non-destructive technology that can be used in conjunction with other, more complex, and expensive technologies to identify the sequence of line crossing. It can provide a novel way of analysis based on the assessment of microelement analysis and the characteristics of diverse ink handwriting samples.

Signature recognition is the process of verifying a writer’s identity by checking the signature against samples previously stored in the database. Several techniques such as the distance-based and statistical classifiers used for feature extraction on a signature image are not invariant to scaling and rotation and the Scale invariant feature transform (SIFT) though invariant to scaling and rotation cannot cater for intra-class variation (Transposition) among set of genuine signature images. This paper proposed a SIFT-SURF algorithm which is used for enhanced offline signature recognition. The SIFT-SURF algorithm computes integral image; obtains hessian data and interest point for each computed integral image; applied neuro-scaling PCA based radial basis function neural network to compute the optimal features for each signature image to come up with an algorithm that is invariant to scaling and rotation as well as reliably match transposition among genuine samples of a signature imag...

Handwritten signatures are very useful in many applications. Such as in the field of bank-cheque processing, document authentication, ATM access, etc. Simultaneous signature verification and forgery detection are also important to avoid misuse and prove the identity of the person signing the document. In this paper, we discuss how the problem has been tackled over the past few decades and analysing the latest progress in this field.

Face recognition is a fast growing and crucial area of study due to its applicability in different life applications. Face recognition systems often face significant challenges due to poor-quality datasets, which can result from factors such as low illumination, low resolution, facial distortions, and varying environmental conditions. These limitations hinder the performance and reliability of face recognition technologies. Generative Adversarial Networks (GANs) have emerged as a powerful tool to address these issues by enhancing image quality and generating realistic facial representations under challenging conditions. This review paper explores various GAN-based models each designed to tackle specific face recognition challenges. These models demonstrate significant improvements in enhancement ratios, ranging from 75% to 97%, by leveraging advanced generative techniques to reconstruct and augment facial images. Beyond face recognition, the methodologies discussed in this research have broader implications for enhancing image datasets in other domains, such as medical imaging, surveillance, and autonomous driving, where poor data quality is a common issue. By improving dataset quality through generative AI, this research paves the way for more robust and accurate machine learning applications across diverse fields.

In this paper an efficient off-line signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of Local Binary Pattern (LBP) based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual's handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark offline English signature dataset (GPDS-300) and a large dataset (BHSig260) composed of Bangla and Hindi off-line signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.

A signature verification system is of two types: Offline and Online. Its main application is in detecting fraud in banks and other organizations. Several approaches are designed till date and all approaches have their own advantages and drawbacks. We have designed our offline signature verification system using Kohonen neural networks. Kohonen networks a type of self-organizing maps are widely used in handwriting recognition systems. This research work makes use of their competitive learning power to quantify the intra-variability of the individual’s signatures. Further paper shows some of the major results achieved in the last few years in the field of off-line signature verification. Based on various research papers a comparison is drawn using important factorsFAR, FRR and ERR. Keyword: Kohonen Networks, Offline Signature Verification.

The purpose of this study was to identify and establish the movements in writing samples
of patients attending cardiology clinics, in support of the hypothesis that early detection of heart related
problems is possible by identifying the common traits.  61 patients attending the cardiac clinics and
41 people, known to not have any form of diagnosed cardiac disease were used as subject and
control group.  Writing samples were taken, and a letter by letter detailed analysis carried out using
an Eschenbach ELG01 reader viewed on 2 separate monitors giving magnification factors from 6x to 11x.
Various writing traits were analysed including pressure on the paper, shape and malformation of the
letter ‘o’, breaks in the letter formation and resting dots, the placing, number and on which letters
they appeared.  A chi-squared test showed no significant differences between the patients and the
control group in the number of breaks, malformed ‘o’s and pressure.  A t-test showed that the mean
ratio of resting dots to letters in the handwriting of patients was significantly higher than in the controls.
The results suggest that the handwriting of patients attending the cardiac clinic show more resting dots,
particularly in the letters a, e and o.

As digital documents and signatures become more prevalent, the field of forensic document examination must adapt to detect forgeries effectively. Traditional methods, such as ink analysis and stroke pattern recognition, are often unsuitable for digitally signed or scanned documents. This paper presents a novel method for detecting forgeries by analyzing HEX/RGB color discrepancies in digital documents. By using Euclidean distance to measure the variation between RGB values of different parts of a signature or document, this method quantifies color inconsistencies, providing a non-invasive, objective means of determining forgery. The results indicate that this approach has strong potential to complement or replace traditional forensic methods in the digital age.

In this paper, we propose a writer dependent approach for offline signature verification based on writer-specific features and cluster-specific classifiers. In this work, writer-dependency is exploited at three levels: features, classifiers, and clusters. Initially, a template signature is selected for each writer from the training samples of that writer. This template signature serves as a representative signature of the respective writer. The relevant features for each writer are chosen using a filter-based feature selection method. The writers are then clustered based on their similar characteristics using the k-means algorithm. After clustering, a cluster-specific classifier is identified. This classifier is then set as the default classifier for all the writers in that cluster. During verification, writer-specific features and cluster-specific classifiers of the claimed writer are used to verify the authenticity of the given test signature. The approach is verified on three benchmarking offline signature datasets: CEDAR, MCYT, and GPDS-960.

Authorizing handwritten signature has always been a challenge to prevent illegal transactions, especially when the forged and the original signatures are very "similar-looking" in nature. In this paper, we aim to automate forged signature verification process, offline, using Adaptive Resonance Theory type-2 (ART-2), which has been implemented in "C" language using both sequential and parallel programming. The said network has been trained with the original signature and tested with twelve very similar-looking but forged signatures. The mismatch threshold is set as 5%; however, it is set flexible as per the requirement from case-to-case. In order to obtain the desired result, the vigilance parameter (ρ) and the cluster size (m) has been tuned by carefully conducted parametric studies. The accuracy of the ART-2 net has been computed as almost 100% with ρ = 0.97 and m = 20.

This monograph focuses on the precarious status of the handwritten signature as a sign of civic identity. On the one hand, a handwritten signature legally introduces the relationship of identity between the signer’s civic name and the shape of it traced in her handwriting. On the other hand, it can never achieve identity: because the trace is different each time, a handwritten signature makes identification of the signer impossible. Following the double strategy of Derrida’s deconstruction, I propose to understand this double bind of our expectation of legal handwritten signature as a complex metaphysical aporia.

Handwriting is a neuro-muscular phenomenon and is unique in nature. The potential of hand in designing the writing involves complex mechanism of the unite movement of finger's, thumb and hand which is operated by the timed neural response system. It's been said that any two objects greater than molecular size have some variation. And so it's with the handwriting. We not at all write anything absolutely the same. The way a person moves their body determines how differently their skills can differ. Every element of writing is movement, and the more a writer moves, the more skilled they are. These subtle variations in personal traits define the boundaries of a person's handwriting.. Therefore, the purpose of this research is to study the hypothesis 'higher the movement higher the skill'. For the analysis the handwriting sample were taken from the individual who do weightlifting or gym and half sample were taken from the normal people who don't prefer gym. Results indicate a massive difference in variation of the handwriting of the weightlifters and non-lifters, due to stiffness of muscles there is less movement in their body while writing.

Numerous types of cases are encountered all over the world in respect to handwriting and signature examination. The cases of forgery, disguise and fraudulent documents are quiet common. Forgery is usually done by person other than the genuine writer, whereas disguise is done by the same individual to conceal one’s own identity in commission of fraudulent activities. The forged handwritings varies from the genuine writing in characteristics such as writing slant, writing alignment, proportional size of letters, formation of letters, and introduction of tremrous and hesitation sub-consciously. Previous researches have proved handwriting as ‘Brain Writing’, indicating handwriting as a personal identification feature of any individual. In handwriting examination these characteristics are identified, examined and compared to prove the authenticity of disputed handwriting or signature. Present research is conducted on the identification and examination of such characteristics on real and prepared forensic cases of disguise and forgery using specifically design “Forensic Doc scale”. The outcomes of the study have proven this innovative design as an important tool for forensic document examiner to ascertain such handwriting characteristics and provided conclusive comparison in disputed case
of forgery and disguise.

According to the American National Science and Technology Council (NSTC), the first signature recognition system was developed in 1965. Then the research continued in 1970 focusing on the potential of geometric characteristic of a signature rather than dynamic characteristic. Nowadays, signature is a commonly used identification procedure. Everyone would be required having a signature for authorization and other important tasks that needs identification. Thus, signature has become one of a method to represent its writer uniquely. Signature has many hidden features that are difficult to extract. Some of the identified features that a signature should have are slanting, baseline, proportion and size. This paper covers the area of signature slant identification. Signatures are captured using a tablet and saved in a digitized format of x and y values. Then it is filtered and calculated for its angle and degree. In the end the signature will be classified to its slant category. A slant algorithm is created and coded into a functional system. An experiment consisting of 50 signatures are tested and the finding shows the angle and degree of the slant in every signature. The result is then tested for its accuracy with an available 10 sample of created proofed signatures. The result shows a favorable accuracy of 80% correct slant identification. The creation of this algorithm would be able to give some degree of contribution in the area of signature recognition.

Standard Hidden Markov Models (HMM) have proved to be a very useful tool for temporal sequence pattern recognition, although they present a poor discriminative power. On the contrary Neural Networks (NNs) have been recognized as powerful tools for classification task, but they are less efficient to model temporal variation than HMM. In order to get the advantages of both HMMs and NNs, different hybrid structures have been proposed. In this paper we suggest a HMM/NN hybrid where the NN classify from HMM scores. As NN we have used a committee of networks. As networks of the committee we have used a Multilayer Perceptron (MLP: a global classifier) and Radial Basis Function (RBF: a local classifier) nets which drawn conceptually different interclass borders. The combining algorithm is the TopNSeg scoring method which sum the top N ranked networks normalized outputs for each class. The test of above architecture with speech recognition, handwritten numeral classification, and signature v...

In recent education system, project submission is crucial for college students to complete their respective studies. The understudies needed to propose their undertaking before finishing the pre-last year. One of the critical assessment forms like course Project Registration System (PRS) helps the students and their education board to enhance the knowledge and skill level required for competitive world.PRS is the efficient methods to authenticate the proposal utilizing the online digital signature recognition criteria. During project submission, authentication is important to prevent the unauthorized submission of proposal and contrast the signature utilizing classification techniques such as Kernel Based Artificial Neural Network (K-ANN), Kernel Based K-Nearest Neighbor (K-KNN), Kernel Based Self Organizing Map (K-SOM) and Kernel based Support Vector Machine (K-SVM). The prime objectives of this research work was to recognize the genuine online digital signatures from the assortmen...

Fly ash is generated during the combustion of coal and rice husk ash is generated by the combustion of rice husk for energy production. Fly ash is industrial waste and rice husk ash is agricultural waste that is widely available worldwide. The utilization of fly ash, rice husk and rice husk ash in various applications has done a great deal to solve the waste problem. The present paper gives a short review of the utilization of fly ash, rice husk and rice husk ash as a composite material. The developed composite material is used in various applications, such as in the automobile industry and in other applications.

Handwritten signatures are very useful in many applications. Such as in the field of bank-cheque processing, document authentication, ATM access, etc. Simultaneous signature verification and forgery detection are also important to avoid misuse and prove the identity of the person signing the document. In this paper, we discuss how the problem has been tackled over the past few decades and analysing the latest progress in this field.

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase...

Background: Since evolution handwriting is one of the most efficient acquired characteristic by any individual. It is an amalgamation of nature and nurture. Learning begins prenatally, and children are not only “ready to learn” but already actively learning from the time they are born.  Parents lay the foundation stone of their child’s pre-writing skills. Moreover, genes also play a crucial role in determining certain characteristics of handwriting. Result: The present study focuses on determination of similarities in handwriting among three generations in 100 families through analysis of pre-defined class characteristics.  It was observed that the “Alignment” of handwriting show high level of similarity in two generations whereas the “Movement” was the only handwriting characteristics that show high level of similarity in all the three generations. Conclusion: Therefore, to conclude it can be inferred that the father-son relationship (grandfather & father or father & son) depicts m...

This paper summarizes a system for recognizing isolated Urdu characters using advanced machine learning algorithms. The system analyzes visual features of Urdu characters, like strokes and curves, to train models such as CNN, SVM, ANN, and MLP. With a large dataset, the system can accurately predict unseen characters. It can be integrated into various applications for real-time character recognition tasks like OCR (Optical Character Recognition) and handwriting recognition. This literature survey explores research papers focused on character recognition in languages like Urdu, Arabic, Persian, and Sindhi, proposing various techniques like feature extraction, deep learning, and machine learning to enhance character recognition technology. The survey highlights specific studies with high accuracy and discusses recognition systems for Arabic characters as well.

This paper summarizes a system for recognizing isolated Urdu characters using advanced machine learning algorithms. The system analyzes visual features of Urdu characters, like strokes and curves, to train models such as CNN, SVM, ANN, and MLP. With a large dataset, the system can accurately predict unseen characters. It can be integrated into various applications for real-time character recognition tasks like OCR (Optical Character Recognition) and handwriting recognition. This literature survey explores research papers focused on character recognition in languages like Urdu, Arabic, Persian, and Sindhi, proposing various techniques like feature extraction, deep learning, and machine learning to enhance character recognition technology. The survey highlights specific studies with high accuracy and discusses recognition systems for Arabic characters as well.

In this paper, we consolidate the gist of unimodal algorithms and their performance in signature verification system. Signature is an ultimate biometrics to authenticate an individual. The signatures are used in cheques, legal transactions etc to determine the individual identity. The legalization of any document takes place by putting a signature on it. This problem can be dealt in two ways: 1) Online signature verification 2) Offline signature verification in some transactions online signatures are used where the user is provided with a pen based tablet. The user has to do his signature on that tablet then that signature will be recorded in the system/computer. Signature trajectory, pen pressure, pen downs & pen ups will be captured by the tablet & sent to the system/computer. Then it will verify with the database whether it is genuine or forged one. On the other hand offline signature is the one which can be obtained by signing on the simple paper & then scanning it to the comput...

a number of biometric techniques have been proposed for personal identification in the past. Among the vision-based ones are face recognition, fingerprint recognition, iris scanning and retina scanning. Voice recognition or signature veri fication are the most w idely known among the nonvision based ones.As signatures continue ti play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. A signature by an authorized person i s considered to be the "seal of approval " and remains the most pr eferred means of authentication.The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verifcation. A veri fication stage includes appl ying the extracted features of test signature to a trained neural network which w ill classify it as a genuine or forged.

A new approach for High Pressure Pixel Polar Distribution on Off-line Signature Verification individual. The robustness of the analyzed system for simple forgeries is tested out with Support Vector Machines models. For the sake of completeness, a comparison of the results obtained by the proposed approach with similar works published is presented.

Signature can be seen as an individual characteristic of a person which, if modeled with precision can be used for his/her validation. An automated signature verification technique saves valuable time and money. The paper is primarily focused on skilled forgery detection. It emphasizes on the extraction of the critical regions which are more prone to mistakes and matches them following a modular graph matching approach. The technique is robust and takes care of the inevitable intra-personal variations. The results show significant improvement over other approaches for detecting skilled forgery.

The most common secure personal authentication in biometrics is handwritten signature. It's widely used in many felids as banks , business transactions , and documents which are being authorized via signatures. The main challenging problem in design offline signature verification system is the phase of extracting features that distinguish between forged and genuine signatures. In this paper, a novel feature of extraction method based on static image splitting is proposed. The center of density of the signature image is used for the splitting. In the proposed system, a new feature called Pixel Length (F4)is suggested. This feature is used in combination with other three features: Pixel Density (F1), Cell Angle (F2), and Pixel Angle (F3) which are common features in the offline verification signature process. Euclidean distance measure was used for classification. The proposed system is implemented and tested using GPDS database. The performance of the proposed system is measured and the experimental results show the usefulness and effectiveness of the proposed system.

The research reported in this thesis investigated cognitive-linguistic predictors of reading comprehension (both within and across languages) among multilingual primary school children in the Punjab region of India. The participants of this study learn three languages: Punjabi, Hindi and English; and are exposed to all three from the initial stage of literacy acquisition. Unlike English, the Punjabi and Hindi orthographies are written nonlinearly with a horizontal bar on the top of the aksharas that connects aksharas within a word, and include vowel symbols that have independent and dependent forms. Both Punjabi and Hindi are alphasyllabic orthographies, whereas English is an alphabetic orthography. Over 400 trilingual school children in Punjab (India) from grades 2 to 5 completed a measure of text reading comprehension that comprised passages followed by questions about details in those passages. Reading comprehension levels were compared to the measures of listening comprehension, phonological processing, orthographic knowledge and speed of processing. Analyses indicated the Punjabi, Hindi and English reading comprehension levels were predicted by measure of listening comprehension and word decoding, with the latter being predicted by phonological and orthographic skills. Such findings were consistent with current models of reading derived from studies of English. However, in contrast to these models, measures of orthographic skills were also predictive of variance in reading comprehension independent of word decoding across Punjabi, Hindi and English models. Contributions of phonological processing and speed of processing were also observed in the English reading comprehension model, again independent of word decoding processes. Overall, Punjabi and Hindi reading comprehension was predicted by similar predictors, with English reading comprehension showing more variations in predictors. Further analyses investigated the influence of Punjabi and Hindi cognitive-linguistic skills on English reading levels. The findings indicated that, in the younger cohorts of students who are more likely to have less reading experience, the influence of Punjabi and Hindi measures on English was limited to word recognition. However, once these multilingual children acquire more expertise in decoding skills (i.e., in the older cohort), listening comprehension, orthographic knowledge and phonological processing in Punjabi and Hindi influenced levels in English reading comprehension. The overall findings from this thesis were used to derive three multilingual models of Punjabi, Hindi and English and one cross-linguistic model of English reading comprehension. These models suggest that a simple view of reading could be applied to Punjabi and Hindi orthographies in a similar way to English. However, additional influences of orthographic knowledge for all three languages (Punjabi, Hindi and English) in such multi-literate learners will need to be taken into account. Additionally, the influence of first and second language skills will need to be considered when developing models of third language reading comprehension. The proposed four models that includes the additional factors are discussed in light of previous research and theories/models in the field. v

Handwritten signatures are very useful in many applications. Such as in the field of bank-cheque processing, document authentication, ATM access, etc. Simultaneous signature verification and forgery detection are also important to avoid misuse and prove the identity of the person signing the document. In this paper, we discuss how the problem has been tackled over the past few decades and analysing the latest progress in this field.

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The existence of identity theft in society has become a major concern due to the effects it causes to those that are affected by it, more especially in the financial sector. Thus this thesis establishes the existence of identity theft issues in the financial sector loan sections and proposes an algorithm that addresses the mitigation processes of identity theft by having the signatures on the loan forms verified using the implementation of the proposed algorithm, then the results are compared with the human experts verification that are done on a daily basis. From the qualitative data collected from the four SACCOs presented indicate the 93% of the respondents knew that forgery of one’s signature in the SACCO exists and from the 93%, 95% of them had been victims of identity theft and 50% of them knew it after deductions were been made from their accounts. The algorithm was implemented in a prototype that was used to test the signatures that were corrected from various individuals th...

Handwriting recognition using OCR (Optical Character Recognition) is a transformative technology that is rapidly changing the way we interact with handwritten documents. OCR technology has traditionally been used for scanning printed text, but with advancements in machine learning and computer vision, it is now possible to recognize and digitize handwritten text as well. This has immense practical implications, as it enables handwritten notes, letters, and documents to be easily searchable, editable, and shareable in digital formats[1]. However, despite its potential, handwriting recognition using OCR is still a relatively nascent technology and faces several challenges. One of the main challenges is the recognition of handwriting styles that vary widely across individuals and cultures. Another challenge is interpreting handwriting that is difficult to read, such as cursive writing or handwritten notes with smudges or crossed out words. Additionally, OCR software is highly dependent on the quality of the input image, making it important to optimize the lighting and capture settings for accurate results.[2] As OCR technology continues to improve, it has the potential to revolutionize the way we interact with handwritten documents and usher in a new era of digital transformation.

This paper presents a simple method for the design of Chaotic Linear Feedback Shift Register (CLFSR) system. The proposed method is based on a combination of two known systems. The first is called Linear Feedback Shift Register (LFSR) system, and the other is called Chaotic Map system. The main principle of the proposed system is that, the output of the LFSR is modified by exclusive-or (XOR) it with the stream bit that is generated by using the chaotic map system to eliminate the linearity and the repeating in the output of the LFSR system. The proposed system is built under Matlab environment and the quality of sequence generation tested by using standard tests which shows that the proposed system is a good random number generator that overcome the linearity and repeating disadvantages.

The sole objective of a forensic document examiner is to assist in discovering and proving the facts in any investigation or legal inquiry involving genuineness of a document or any part thereof. A document is usually questioned because its origin, content, or the circumstances and story of its production arouse serious suspicion as to its genuineness. It may also be adversely scrutinized simply because it displeases someone with its unexpected provisions, and a careful examination may show conclusively that the document is indeed genuine. 1 The increasing use of many kinds of documents is among NOWA KODYFIKACJA PRAWA KARNEGO Tom LXIV

The sole objective of a forensic document examiner is to assist in discovering and proving the facts in any investigation or legal inquiry involving genuineness of a document or any part thereof. A document is usually questioned because its origin, content, or the circumstances and story of its production arouse serious suspicion as to its genuineness. It may also be adversely scrutinized simply because it displeases someone with its unexpected provisions, and a careful examination may show conclusively that the document is indeed genuine. 1 The increasing use of many kinds of documents is among NOWA KODYFIKACJA PRAWA KARNEGO Tom LXIV

Outcome-based education is a pedagogical process which focuses on the achievement of certain specified results. Outcome-based teaching and learning (OBTL), therefore, is concerned with curriculum design and ensuring that the contents, delivery, activities, and assessments are all aligned to help facilitate students to attain specific intended learning outcomes. Outcome-based education (OBE) is an educational theory that bases each part of an educational system around goals (outcomes). By the end of the educational experience, each student should have achieved the goal. There is no single specified style of teaching or assessment in OBE; instead, classes, opportunities, and assessments should all help students achieve the specified outcomes. The role of the faculty adapts into instructor, trainer, facilitator, and/or mentor based on the outcomes targeted. History of Outcomes-Based Teaching and Learning (OBTL) Outcome-based methods have been adopted in education systems around the world, at multiple levels. Australia and South Africa adopted OBE policies in the early 1990s but have since been phased out. The United States has had an OBE program in place since 1994 that has been adapted over the years. In 2005, Hong Kong adopted an outcome-based approach for its universities. Malaysia implemented OBE in all of their public schools systems in 2008. The European Union has proposed an education shift to focus on outcomes, across the EU. In an international effort to accept OBE, The Washington Accord was created in 1989; it is an agreement to accept undergraduate engineering degrees that were obtained using OBE methods. As of 2017, the full signatories are