Handwritten Text Verification on Mobile Devices

International Journal of Interactive Mobile Technologies (iJIM), 2020

This research presents a methodology for user identification using ten English words handwritten on smartphone and mini-tablet. This research considers three features, namely Signature Precision (SP), Finger Pressure (FP), and Movement Time (MT) that were extracted from each of the ten English words using dynamic time warping. The features are then used individually and combined for the purpose of user identification based on the Euclidean distance and the k-nearest neighbor classifier. We concluded that the best identification accuracy results from the combinations of the SP and the FP features with average accuracies of 74.55% and 69% were achieved on small smartphone and Minitablet respectively using a dataset of 42 users Keywords-User identification, user identification on smartphone, security on smartphone, Dynamic time warping, dynamic features, mobile computing, and handwriting based finger on smartphone.

International Journal of Interactive Mobile Technologies (iJIM)

This research presents a methodology for user identification using ten English words handwritten on smartphone and mini-tablet. This research considers three features, namely Signature Precision (SP), Finger Pressure (FP), and Movement Time (MT) that were extracted from each of the ten English words using dynamic time warping. The features are then used individually and combined for the purpose of user identification based on the Euclidean distance and the k-nearest neighbor classifier. We concluded that the best identification accuracy results from the combinations of the SP and the FP features with average accuracies of 74.55% and 69% were achieved on small smartphone and Minitablet respectively using a dataset of 42 users Keywords-User identification, user identification on smartphone, security on smartphone, Dynamic time warping, dynamic features, mobile computing, and handwriting based finger on smartphone.

2011

On-line handwritten signature-based personal authentication is still a challenging research topic. Although great efforts have been achieved in developing and defining a framework that systems for on-line authentication based on the handwritten signature of a person should adhere, these frameworks are still not enough because they do not include all the features that handwritten signature as a biometric feature has. In addition, there is a range of features that current system for capturing a signature posses by which it is possible to further and better define the characteristics of signatures required to be in the process of authentication of entities, but can be used not only for authentication but also for identification. The paper provides an overview of the differences between off-line and on-line mode of authentication system based on the handwritten signature as well as surveys of some properties that these features can have, supported with the latest systems to "capture" handwritten signatures.

2003

Abstract. Most people are used to signing documents and because of this, it is a trusted and natural method for user identity verification, reducing the cost of password maintenance and decreasing the risk of eBusiness fraud. In the proposed system, identity is securely verified and an authentic electronic signature is created using biometric dynamic signature verification. Shape, speed, stroke order, off-tablet motion, pen pressure and timing information are captured and analyzed during the real-time act of signing the handwritten signature. The captured values are unique to an individual and virtually impossible to duplicate. This paper presents a research of various HMM based techniques for signature verification. Different topologies are compared in order to obtain an optimized high performance signature verification system and signal normalization preprocessing makes the system robust with respect to writer variability. 1

2012 International Conference on Frontiers in Handwriting Recognition, 2012

Recently, research in handwritten signature verification has been considered with renewed interest. In fact, in the age of e-society, handwritten signature still represents an extraordinary means for personal verification and the possibility of using automatic signature verification in a range of applications is becoming a reality. This paper focuses on some of the most remarkable aspects the field and highlights some recent research directions. A list of selected publications is also provided for interested researchers.

Lecture Notes in Computer Science, 2003

Most people are used to signing documents and because of this, it is a trusted and natural method for user identity verification, reducing the cost of password maintenance and decreasing the risk of eBusiness fraud. In the proposed system, identity is securely verified and an authentic electronic signature is created using biometric dynamic signature verification. Shape, speed, stroke order, off-tablet motion, pen pressure and timing information are captured and analyzed during the real-time act of signing the handwritten signature. The captured values are unique to an individual and virtually impossible to duplicate. This paper presents a research of various HMM based techniques for signature verification. Different topologies are compared in order to obtain an optimized high performance signature verification system and signal normalization preprocessing makes the system robust with respect to writer variability.

Cognitive Computation

Online handwritten analysis presents many applications in e-security, signature biometrics being the most popular but not the only one. Handwriting analysis also has an important set of applications in e-health. Both kinds of applications (e-security and e-health) have some unsolved questions and relations among them that should be addressed in the next years. We summarize the state of the art and applications based on handwriting signals. Later on, we focus on the main achievements and challenges that should be addressed by the scientific community, providing a guide for future research. Among all the points discussed in this article, we remark the importance of considering security, health, and metadata from a joint perspective. This is especially critical due to the risks inherent when using these behavioral signals.

International Conference on Security and Cryptography (SECRYPT'07), 2007

There is considerable interest in authentication based on handwritten signature verification because it is superior to many other biometric authentication techniques such as finger prints or retinal patterns, which are reliable but {\it much more intrusive}. The paper details a number of experiments using a signature verfication technique which is unlike any other reported in literature. Specifically, characters are used to represent various features of a signature image allowing the use of existing and proven string distance metrics to determine distances between signatures. Extensive testing shows that our proposed system is comparable with, and in many aspects better than the highest quality signature verfication techniques presented in literature.

With the increasing concerns for security, automated systems for authorization and authentication have become enormously important in every sector today. There are many methods for personal identification such as smart cards, PIN (personal Identification Number), passwords, etc. Regardless of the efficiency and accuracy of these systems, these systems can be always be stolen, lost, forgotten, cracked, hacked, etc. And it is for this reason biometric based authentication system have gained a lot of importance worldwide. A biometric system is essentially a pattern-recognition system that recognizes a person based on a feature vector derived from a specific physiological (face, iris, retina, voice, palm prints, hand geometry) or behavioral characteristic (signature, voice, keystroke pattern) that the person possesses. This system is more accurate as these characteristics are unique for a particular person and vary almost negligibly over time. In this paper we have presented a comparative study of recent advances in biometric authentication based on mainly offline Hand-written signatures.

Pattern Recognition, 2012

This paper presents a handwritten text biometric recognition system suitable to be applied to short sequences of text (words). Strokes are considered the structural units of handwriting with words being regarded as two separate sequences: one of pen-down and one of pen-up strokes. Unsupervised categorization by means of a self-organized map allows mapping strokes to integers and the efficient comparison of the sequences by means of dynamic time warping. Measures obtained from each sequence are combined in a later step. This separation gives us the opportunity to show that pen-up strokes possess a surprisingly high discriminative power, while the performance of the combination suggests they may carry non-redundant information with respect to pen-down strokes.