A NEW TECHNIQUE TO PROCESS AND RECOGNIZE BARCODES USING I~DUCTION

[n this paper, a new technique to recogmze and process Barcodes is introduced. [he technique employs Inductive Learning. It is suitable to use, for example, in a factory to control the workers, staff, stock etc. In this technique only vertical lines are considered whIle the spaces in between are Ignored. This results faster processing. Each Barcode is considered to represent an item. For each Barcode a rule IS extracted from the necessary information using Inductive Learning. So the unnecessary information is eliminated. This causes faster processing time and less amount of memory In order to use this techmque no special hardware IS required Only a PC and a Barcode reader is enough


A NEW TECHNIQUE TO PROCESS AND RECOGNIZE BARCODES USING I~DUCTION
[n this paper, a new technique to recogmze and process Barcodes is introduced.
[he technique employs Inductive Learning.It is suitable to use, for example, in a factory to control the workers, staff, stock etc.In this technique only vertical lines are considered whIle the spaces in between are Ignored.This results faster processing.Each Barcode is considered to represent an item.For each Barcode a rule IS extracted from the necessary information using Inductive Learning.So the unnecessary information is eliminated.This causes faster processing time and less amount of memory In order to use this techmque no special hardware Barcodes are made of some thin and bold bars, some spaces in between and some meanmgful numbers (1].They can be used to recognize some items such as the type, price etc. of a product; the names and other necessary information of staffs in a factory; the names, subjects, author's names, year and other information of a book or publication in a library and so on.For example in a factory the worker's and staff's comings and goings can be controlled and for example payroll can be designed using barcodes.In order to do these the barcode must be read and processed Normally a barcode is read by a reader and recognized using a special hardware which employs a special technique [2].
In this paper a new techmque to process and recognize Barcodes is introduced.The technique employs Inductive Learning.In the paper RULES-3 inductive learning algorithm is introduced For a number of randomly generated barcodes, how the necessary rules are extracted and using the extracted rules how barcodes are recognized are explained.
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 the process of generalizing a procedural description from presented or observed examples [3,4,5].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 explains the training examples and that may also be used to classify previously unseen cases.
RULES-3 [6] 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 {-I 0, 10}.
An attribute-value pair constitutes a condition in a rule.If the number of attributes is N a ' a rule may contain between one and N a conditions.Only conjunction of conditions is permitted in a rule and therefore the attributes must all be 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 attribute-value 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 N a 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 exammatIOn 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 contmues until an 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.I.
] Barcode25 26 ] ] Using RULES-3, 30 rules can be extracted from the set of examples given in Table1.The rule set (knowledge base) IS given in Table 2.As can be seen from Table 2, none of the rules contains more than two conditions and even some of them have only one condition.It shows that out of 20 possible conditIons only one or two of them are enough to represent and recognize each barcode while the rest are not necessary for this application.This helps to spent less effort and time to store, recognize and process a barcode The unnecessary information is elimmated by means of inductive learning.

Step 5
more than one candIdate rule IS formed for an example, the rule which 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 untll there are no examples left in the collection and all objects have been classified.ThiS algorithm can be summarizes as follow Step 1. Define ranges for the attributes \,hich have numerical values and assign labels to those ranges Step 2. Sct the minimum number of conditions (Ncmin) for each rule Step 3. Take an unclassified cx.ample Step 4. N c = Ncmin -1 IfN c < N a then N c = N c + I Step 6.Take all values or labels contained in the example Step 7. Form objects which are combinations orNe values or labels taken from the values or labels obtained in Step (j Step 8. Ifat least one of the objects belongs to a unique class then form rules with those objects: ELSE go to Step 5 Step 9 Select the rule which classifies the highest number of examples Step 10 Remove examples classified by the selected rule Step 11 If there are no more unclassified examples then STOP: ELSE go to Step 3 For this application it is assumed that there are 20 attributes.Each attribute is considered to represent the thIckness of a vertical line The vertical lines in a barcode are numbered from left to right for each barcode Each line represents an attribute For example the first line represents Attribute-l (or AI in short), the second A2 etc The value for each attribute can vary from I to 5 pixels In this work the set of examples consIsts of 30 randomly generated barcodes Only the thickness of lines are considered while the spaces In between are ignored.Each example consists of20 values for each barcode For instance the following example represents Barcode-I' It means the thickness for the first line in the Barcode-I IS 4 pixels, for the second it is 3 pixels and so on The whole set of randomly generated examples is given in Table