Determinación de propiedades de trazos manuscritos por medios interferométricos

El procesamiento automático de trazos realizados por seres humanos ha sido aplicado en gran cantidad de situaciones tales como el reconocimiento de texto manuscrito, el procesamiento digital de firmas y el reconocimiento de la caligrafía entre muchas otras. Desde larga data se ha reconocido la importancia de la presión que ejerce el escribiente sobre el papel en cada fragmento del trazo. El uso de esta información ha estado restringido a dispositivos de captura que sólo pueden obtener la información necesaria si el escribiente utiliza un instrumento de escritura especial. En muchos casos sería muy útil disponer de información acerca de la presión ejercida durante la realización de un trazo luego que la escritura se haya realizado. Dependiendo del papel, del instrumento utilizado y de la superficie sobre la que estaba colocado el mismo, se obtienen variaciones en el grosor del trazo y deformaciones en el papel que pueden ser medidas o estimadas. En particular, se han hecho experiencias que prueban que las técnicas interferométricas son muy aptas para determinar la deformación de papel producida por la escritura manuscrita. En el presente proyecto se aspira a integrar las técnicas interferométicas con la información de grosor de trazo y los datos clásicos utilizados en el procesamiento de la escritura manuscrita.Eje: AlgoritmosRed de Universidades con Carreras en Informática (RedUNCI

Perfilometría virtual en trazos manuscritos residuales

El proyecto “Determinación de Propiedades de Trazos Manuscritos por Distintos Medios” propone estimar el grado de presión o el grado de presión relativa empleado en la escritura manuscrita, en distintas partes del trazo. Usando un método no invasivo y de bajo costo. Que el texto original no se modifique física o químicamente permite la posibilidad de múltiples análisis, también el caso de análisis forense donde existe la necesidad de preservar la muestra original. En este trabajo se propone a través del procesamiento de imágenes, inferir la presión ejercida cuando una persona escribe, analizando las diminutas deformaciones que la escritura produce sobre el papel y las características del trazo tales como el grosor y valor de gris del mismo.Eje: Computación gráfica, imágenes y visualizaciónRed de Universidades con Carreras en Informática (RedUNCI

A Colour Code Algorithm For Signature Recognition

The paper "A Colour Code Algorithm for Signature Recognition" accounts an image processing application where any user can verify signature instantly. The system deals with a Colour code algorithm, which is used to recognize the signature. The paper deals with the recognition of the signature, as human operator generally make the work of signature recognition. Hence the algorithm simulates human behavior, to achieve perfection and skill through AI. The logic that decides the extent of validity of the signature must implement Artificial Intelligence Pattern recognition is the science that concerns the description or classification of measurements, usually based on underlying model. Since most pattern recognition tasks are first done by humans and automated later, the most fruitful source of features has been to asked the people who classify the objects how they tell them a part . Signatures are a behavioural biometric that change over a period of time and are influenced by physical and emotional conditions of a subject. In addition to the general shape of the signed name. The algorithm is tested on various operating systems & we find that it works very well & satisfactory. While implementing the recognition process, we have used quite simpler way. At this stage we are getting accuracy up to about 80% to 90%

HANDWRITTEN SIGNATURE VERIFICATION BASED ON THE USE OF GRAY LEVEL VALUES

Recently several papers have appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP plus LBP and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier

Non-english and non-latin signature verification systems: A survey

Signatures continue to be an important biometric because they remain widely used as a means of personal verification and therefore an automatic verification system is needed. Manual signature-based authentication of a large number of documents is a difficult and time consuming task. Consequently for many years, in the field of protected communication and financial applications, we have observed an explosive growth in biometric personal authentication systems that are closely connected with measurable unique physical characteristics (e.g. hand geometry, iris scan, finger prints or DNA) or behavioural features. Substantial research has been undertaken in the field of signature verification involving English signatures, but to the best of our knowledge, very few works have considered non-English signatures such as Chinese, Japanese, Arabic etc. In order to convey the state-of-the-art in the field to researchers, in this paper we present a survey of non-English and non-Latin signature verification systems

Verifikasi Tanda Tangan Menggunakan Algoritma Colour Code dan Ekspresi Boolean XOR

Verifikasi tanda tangan adalah proses pemeriksaan keabsahan tanda tangan dengan memasukkan data tanda tangan asli ke dalam media input untuk mendapatkan data berupa image digital (citra) tanda tangan. Data image yang digunakan dalam proses pengenalan dan verifikasi tanda tangan diinputkan ke dalam sistem dengan dua cara, yaitu secara on-line dan atau off-line. Secara on-line, image tanda tangan dapat diperoleh dari sebuah graphics tablet yang dirancang khusus untuk keperluan tulisan tangan. Sedangkan secara off-line, image tanda tangan diperoleh dari hasil scan menggunakan scanner. Image tanda tangan yang telah diperoleh secara online atau offline akan diolah menggunakan teknik Preprocessing Image. Prepocessing Image merupakan suatu proses awal untuk memperbaiki atau meningkatkan image dari hasil pengambilan data melalui scanner atau graphics tablet, agar lebih mudah dikenali untuk aplikasi tertentu. Teknik Prepocessing Image yang akan digunakan adalah Transform BnW, Stretching, Bolding. Pada sistem ini, metode bolding digunakan berulangkali untuk mendapatkan pola uji. Setelah melewati Prepocessing Image, maka image data tangan akan diolah lebih lanjut menggunakan Algoritma Colour Code. Algoritma ini memiliki beberapa tahapan yaitu menghasilkan pola uji untuk image standar, pemindahan test image ke pola uji, pencocokan data uji dengan data sampling menggunakan operasi XOR, analisa pola resultan dan kalkulasi persentasi ketepatan. Dalam sistem, hasil persentase verifikasi dapat dikelompokkan menjadi beberapa kelompok hasil yaitu tanda tangan yang bersifat rejected, okay, acceptable, good, better dan perfect

Offline signature verification scheme using feature extraction method

In this project a new improved offline signature verification scheme has been proposed. The scheme is based on selecting 60 feature points from the geometric centre of the signature and compares them with the already trained feature points. The classification of the feature points utilizes statistical parameters like mean and variance. The suggested scheme discriminates between two types of originals and forged signatures. The method takes care of skill, simple and random forgeries. The objective of the work is to reduce the two vital parameters False Acceptance Rate (FAR) and False Rejection Rate (FRR) normally used in any signature verification scheme. Comparative analysis has been made with standard existing schemes. The Algorithms are based on the Geometric Center of an image so images are splitted into different parts to get the geometric centers of each which are called as Feature points in our thesis. We have taken 60(30+30) Feature points for calculation purpose(in extended Algorithm). As Feature points increases results will be more accurate but complexity and time require for testing will be more. So we have taken 60 feature points which improves security and maintains same complexity level. All calculations are done on the basis of these feature points. Results are expressed in terms of FAR (False Acceptance Rate) and FRR (False Rejection Rate) and subsequently compare these results with other existing Techniques. Results obtained by this algorithm are quite impressive. Random and Simple forgeries are eliminated and skilled forgeries are also eliminated in greater extent. As signature image is tested rigorously so FRR is more in the Algorithm proposed by us

Novel Features for Off-line Signature Verification

In this paper a novel feature extraction scheme has been suggested for offline signature verification. The proposed method used geometric center for feature extraction. Euclidean distance model was used for classification. This classifier is well suitable for features extracted and fast in computation. Method proposed in this paper leads to better results than existing offline signature verification methods. Threshold selection is based on statistical parameters like average and standard deviation (sigma)

Detecting off-line signature model using wide and narrow variety class of local feature

There are so many questioned document cases in Indonesia, mostly related to disputed signatures, both are forgery and denial of the offline signature.The Indonesian forensic document examiners have been examining the signatures manually and they have not been implementing the computer in signatures identification optimally yet.Therefore, it needs help of computer based detection to speed up and support decision making in examining signature forgery.Many research in this field was done, but it still an open research especially in detection accuracy. Usually every detection method only dictates for certain class of forgery and uses only one phase detection.Otherwise, this research proposes two phase detection that has capability for detecting all classes of forgery.This approaches based on hypothesize that the detection of skilled signatures forgery can be identified using a wide variety of segments and random to moderate signature forgery can be identified using a narrow variation of segments.Otherwise, the skilled forgery will be detected using wide variety of local features.For future work, it has to be selected the appropriate segmentation technique to determine the narrow and wide variety area of signature and formula to calculate the distance among signatures