2. BARCODE IDENTIFICATION BY USING WAVELET BASED ENERGY Soundararajan Ezekiel, Gary Greenwood, David Pazzaglia Computer Science Department Indiana University of Pennsylvania Indiana, PA, USA Soundararajan Ezekiel, Gary Greenwood, David Pazzaglia Computer Science Department Indiana University of Pennsylvania Indiana, PA, USA

3. ABSTRACTABSTRACT we present a wavelet-based method for automatic barcode character detection. Barcodes are widely used in a wide array of applications. In order to facilitate barcodes, users must have a method for scanning a barcode. The barcode scanner scans and identifies the characters present in the barcode. Barcode scanners only work if the barcode image is recognizable. In the event of image distortion, it will fail to recognize the characters. Our method overcomes this problem, by reliably identifying the characters using multiresolution analysis. This analysis, removes any existing noise by convoluting various filters. We also, apply morphological operators to fill the gaps that are caused during the noise filtering process.

4. Continue Once these gaps have been filled, we extract the characters. Each character is then compared with a predefined dictionary of characters by using two measures: correlation, multiresolution approximate coefficient energy to find a matching character. Finally, we display the best matched character. The result suggests that this method is effectively capable of being applied to a broad range of barcodes. Since this method is simple, efficient, and has a real- time response, it can be implemented in embedded systems.

5. Introduction Barcodes were created as a replacement for punch cards to identify product details. They have become very popular because of their simplicity and accuracy in the business community Barcode identification systems are also critical elements in today’s global industry These systems optically read a merchandises identification code and transmit this to a computer. The computer then extracts the merchandise details from its database using the identification code.

6. Introduction -- Continue Because of the vastness of the business community, the identification standard consists of many different types of barcodes that have been developed out of necessity. Barcodes have two classifications: one dimension and two dimension These classifications differ both in the way they represent data as well as how they decode the data Some decode the data as letters only, some as numbers only, some combine letters and numbers, and some decode special character strings of various length and type Barcode scanners work well if the barcode being scanned is flat, not wider than the scanner, and the symbols are not distorted.

7. Introduction --- continue However, sometimes the products do not follow the above said conditions. For example, a roll of film or an old receipt does not follow these conditions. If the barcode has become distorted and we cannot visually identify the barcode characters, this will cause a problem. This type of problem can be solved by implementing an image processing system that can efficiently identify the characters in the barcode. we use a wavelet based method for automatic barcode character detection because it is simple, effective, and it can be implemented in embedded systems. This method seems to be well suited for a wide variety of barcodes

8. DCT - Discrete Cosine Transformation –Encode Take image Divide into 8x8 blocks Apply 2-D DCT--- DCT coefficients Apply threshold value Store the hidden message in that place Take inverse– store as image –Decode Start with modified image Apply DCT Find coefficient less than T Extract bits Combine bits and make message 219 215 214 216 218 218 217 216 219 216 216 216 215 215 215 215 217 217 218 216 212 212 213 215 215 215 215 215 211 212 214 216 217 216 214 216 215 215 217 218 216 216 215 214 215 215 215 216 215 214 210 210 211 215 215 216 218 215 211 211 213 214 216 216 1720 1.524 7.683 1.234 1.625 0.9234 -0.07047 -1.055 5.667 3.475 -4.181 -1.524 1.152 1.637 1.016 0.3802 0.3711 -1.442 1.067 5.944 0.3943 -0.4591 0.1313 0.7812 3.888 -3.356 -1.97 3.265 0.5632 -0.939 -0.2434 0.2354 1.625 -2.279 0.4735 1.392 1.375 0.6552 -1.143 0.03459 -4.049 -1.223 0.5466 -0.5425 -1.013 -0.2651 0.5696 -0.9296 1.876 1.924 -1.369 -1.132 -0.02802 -0.4646 0.1831 0.9729 0.8995 -0.7233 0.667 0.436 0.1325 -0.03665 -0.3141 -0.4749

9. Wavelets Transformation Wavelets are basis function in continuous time. a basis is a set of linearly independent functions that can be used to produce all admissible functions f(t) The special feature of wavelet basis is that all functions are constructed from a single mother wavelet w(t). This wavelet is is a small wave ( a pulse). Normally it starts at time t=0 and end at time t=N Compressed = Shifted k time = Combine both we have Haar Wavelet :- 1909 Haar, 1984– theory, 88– daubechies 89- Mallat 2-d, mra, -- 92- bi-orthogonal Haar=

10. Message to be Hidden Carrier WaveletTransformationThresholdingCompression Stego image Error Image Inverse Transformation Extract the Hidden Message figure

11. Energy Measure The energy of an image is calculated by dividing the squared sum of all the approximate coefficients by the number of coefficients i.e. where E is the energy, is the approximate coefficient vector, and N is the number of coefficients

12. Two-dimensional Correlation The two dimensional correlation between two images and of size is computed by the following formula: where and are each a two-dimensional mean.

13. Methodology Step1. start with an image that has to be analyzed. If the image is a color image, convert it to grayscale or analyze red, green, and blue images individually. Then a dictionary of characters is defined for character matching. Further, we calculate energy measure for each character in the dictionary and store them for future comparisons in a vector E. The plot of the vector E is called the energy spectrum. Step2. Next, the appropriate filter type and size is chosen to restore the image by removing the noise. This can be done by convoluting the image with the chosen filter.

15. Continue The convolution process may damage the characters in the barcode image. Damages such as broken characters, thinning or thickening of the edges, and the creation of gaps in the characters may occur. Sometimes these conditions may be preexisting. To address the above said problems, we apply the morphological operations such as: close, open, and fill. These operations will enhance the image resolution and increase the quality of the characters.

16. Continue Then by extracting the characters from the barcode, we calculate the energy measure and compare it with each element in E. This comparison is done by using the thresholding technique, which gives a set of possible matching characters. We then calculate the two dimensional correlation coefficients between the extracted character and each character in the matching set to ensure the best match. Finally, we display the matched character.

17. predefined dictionary of characters

22. Conclusion we have been able to extract characters and find their matching characters successfully. Our experimental results have demonstrated that our algorithm is effective for character restoration, extraction, and matching in an array of barcode images. A foundation for wavelet-based method for automatic barcode character detection has been set forth. The wavelet-based method can be applied to a variety of barcodes such as ISBN, Code 128, Code 39, and others. However, further experimental analysis needs to be carried out for different barcode types, wavelet filters, noise removal masks, and mask sizes to adapt the wide array of existing character features. More information – check out website http://www.cosc.iup.eud/sezekiel Contact: sezekiel@iup.edusezekiel@iup.edu