Nigeria Paper Currency Serial Number Pattern Recognition System for Crimes Control

Only secured and conducive environment void of robbery, kidnapping, fake currency and all forms of insurgencies will foster production and distribution of goods, investment and saving that enhance national economic growth and development. This is a mirage in a country generally believed and tagged the giant of African; Nigeria. Crime, in whatever name or nomenclature, has a significant negative impact on the welfare and economy prosperities of our society. The urge to get rich promotes Crime like armed robbery, kidnapping for ransom and production of counterfeit banknotes to mention but a few. Innocent people have suffered psychological distress, fear, anger, depression, physical harm, financial loss and in most cases untimely death during the operations by these hoodlums. Banks, Cash-In-Transit Vehicle, and ATM points are often robbed by gangs in search for paper currency. Kidnappers as well demand for paper currency as ransom while some other gangs are involved in the production of counterfeit banknotes so as to enrich themselves no minding the negative effect on the nation’s economy.  The banknotes collected during the operations by the hoodlums are taken to banks. Yet, the banks will not detect or recognize any of these notes which attest to the fact that our system lacks check and balance. The system is very porous without a recourse to this era of technology when machine is trained to do virtually everything for our convenience. Currency as an entity has a unique identification number. The identification number is an alphanumeric currency issuance of about 10 digits comprises two (2) capital letters and eight (8) numbers usually positioned at a strategic location on either front or back of the 5, 10, 20, 50, 100, 200, 500 and 1000 naira notes. It is a reliable and intelligent system developed to track banknotes unique identifiers numbers- serial numbers, in order to control financial related crimes. Keywords: Nigeria Paper Currency Serial Number, Pattern Recognition DOI: 10.7176/IKM/11-3-04 Publication date: April 30th 202

Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

Kodikologie und Paläographie im digitalen Zeitalter 2 - Codicology and Palaeography in the Digital Age 2

Der Einsatz digitaler Technik verändert den wissenschaftlichen Umgang mit der handgeschriebenen Überlieferung. Dieser Band vertieft Fragen zu Digitalisierung und Katalogisierung, zu automatischer Schrifterkennung und Schriftanalyse, und er erweitert eine Diskussion, die mit dem im letzten Jahr erschienenen ersten Band zur digitalen Handschriftenforschung angestossen worden ist: Welche Erkenntnisse können etwa naturwissenschaftliche Methoden liefern? Welche musik- und kunsthistorischen Fragestellungen lassen sich mit Hilfe moderner Informationstechnologien beantworten? Wie lassen sich Methoden einer digitalen Auswertung lateinischer Handschriften auf griechische, glagolithische oder ägyptische Texte anwenden? Der raum-zeitliche Rahmen der hier von einer internationalen Autorenschaft zusammengetragenen 22 wissenschaftlichen Beiträge reicht vom alten Ägypten bis ins Paris der Postmoderne. Mit Beiträgen von: Pádraig Ó Macháin; Armand Tif; Alison Stones, Ken Sochats; Melissa Terras; Silke Schöttle, Ulrike Mehringer; Marilena Maniaci, Paolo Eleuteri; Ezio Ornato; Toby Burrows; Robert Kummer; Lior Wolf, Nachum Dershowitz, Liza Potikha, Tanya German, Roni Shweka, Yacov Choueka; Daniel Deckers, Leif Glaser; Timothy Stinson; Peter Meinlschmidt, Carmen Kämmerer, Volker Märgner; Peter Stokes—Dominique Stutzmann; Stephen Quirke; Markus Diem, Robert Sablatnig, Melanie Gau, Heinz Miklas; Julia Craig-McFeely; Isabelle Schürch, Martin Rüesch; Carole Dornier, Pierre-Yves Buard; Samantha Saidi, Jean-François Bert, Philippe Artières; Elena Pierazzo, Peter Stokes. Einleitung von: Franz Fischer, Patrick Sahle. Unter Mitarbeit von: Bernhard Assmann, Malte Rehbein, Patrick Sahle