BANKNOTE RECOGNITION USING INDUCTIVE LEARNING Mehmet Sevkli* Ali Türkyilmaz* M. S. Aksoy** *Fatih University, Faculty of Engineering, Department of Industrial Engineering, Buyukcekmece, 34900, Istanbul, Turkey e-mail:
[email protected] [email protected] **Sakarya University, Faculty of Engineering, Department of Industrial Engineering, Esentepe,Adapazari, Turkey
[email protected] Abstract: In this paper we propose an alternative technique for banknote recognition. The technique employs RULES-3 inductive learning algorithm to extract the necessary set of rules and to recognize a banknote. 10 of 3x3 masks were used to represent a banknote. Each banknote (or example) consists of 10 frequencies of each mask. The system was trained for back and front sides of 5 different Turkish banknotes. The system also was tested using 24 “unseen” examples and correctly classified all of them.
Keywords: Inductive Learning, Banknote Recognition, Machine Learning, Image Processing
1 Introduction Banknotes are still the most widespread means of payment and the main means used in personal transactions. They are state-controlled and usable by everyone, including children. Automatic cash payment is currently on the increase in several market sectors, particularly for those where 24-hour availability of goods and services is important, for example in public transport, petrol stations, car parks, vending machines, etc. Banks use ATMs (Automatic Teller Machine) to provide unlimited and fast services to their customers to let them get what they need using those machines themselves. Besides that, some firms sell their products using those machines. When someone wants to deposit some money into his account using ATM, he should put it in an envelope and leave it to the machine. Afterwards a human being has to control for how much money is in the envelope and put it to the account. What we propose here is to have a unit in an ATM, which can recognize the banknote without human. If a scanner or a camera is installed somewhere in ATM, and when a banknote is fed to the machine it will snap the banknote and send it to the visual inspection system to have a decision what banknote is. The decision is made using RULES-3 inductive learning system. The organization of the paper is as follows: In section 2 RULES-3 inductive learning algorithm is outlined. The operation of the new technique is explained in
section 3. The application areas of the system are given in section 4, conclusion and future work is in the section 5. A simple problem to illustrate the operation of RULES -3 is given in the Appendix. 2 RULES -3 Inductive Learning Algorithm In recent years, there has been a growing amount of research on inductive learning. In its broadest sense; induction (or inductive inference) is a method of moving from the particular to the general - from specific examples to general rules. Induction can be considered as the process of generalizing a procedural description from presented or observed examples. These examples may be specified by an expert as a good tutorial set, or may come from some neutral source such as an archive. The induction process will attempt to find a method of classifying an example expressed as a function of the attributes that exp lains the training examples and that may also be used to classify previously unseen cases. RULES -3 is a simple algorithm for extracting a set of classification rules from a collection of examples for objects belonging to one of a number of known classes. An object must be described in terms of a fixed set of attributes, each with its own range of possible values, which could be nominal or numerical. For example, attribute "length" might have nominal values {short, medium, long} or numerical values in the range {-10, 10}. An attribute-value pair constitutes a condition in a rule. If the number of attributes is Na, a rule may
contain between one and Na conditions. Only conjunction of conditions is permitted in a rule and therefore the attributes must be all different if the rule comprises more than one condition. RULES-3 extracts rules by considering one example at a time. It forms an array consisting of all attributevalue pairs associated with the object in that example, the total number of elements in the array being equal to the number of attributes of the object. The rule forming procedure may require at most Na iterations per example. In the first iteration, rules may be produced with one element from the array. Each element is examined in turn to see if, for the complete example collection, it appears only in objects belonging to one class. If so, a candidate rule is obtained with that element as the condition. In either case, the next element is taken and the examination repeated until all elements in the array have been considered. At this stage, if no rules have been formed, the second iteration begins with two elements of the array being examined at a time. Rules formed in the second iteration therefore have two conditions. The procedure continues until iteration when one or more candidate rules can be extracted or the maximum number of iterations for the example is reached. In the latter case, the example itself is adopted as the rule. If more than one candidate rule is formed for an example, the rule that classifies the highest number of examples is selected and used to classify objects in the collection of examples. Examples of which objects are classified by the selected rule are removed from the collection. The next example remaining in the collection is then taken and rule extraction is carried out for that example. This procedure continues until there are no examples left in the collection and all objects have been classified. This algorithm can be summarized as follows: Step 1. Define ranges for the attributes, which have numerical values and assign labels to those ranges Step 2. Set the minimum number of conditions (Ncmin) for each rule Step 3. Take an unclassified example Step 4. Nc=Ncmin-1 Step 5. If Nc
