USING VISUALIZATION FOR HANDWRITING AND SIGNATURE ANALYSIS. Tushar Bulsara Ashbrook High School

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1 USING VISUALIZATION FOR HANDWRITING AND SIGNATURE ANALYSIS Tushar Bulsara Ashbrook High School Summer Ventures in Science and Math 2008 Visual and Image Processing Dr. Rahman Tashakkori, Mr. Jere Miles Appalachian State University July 2008

2 ABSTRACT USING VISUALIZATION FOR HANDWRITING AND SIGNATURE ANALYSIS Tushar Bulsara Ashbrook High School Dr. Rahman Tashakkori This research focused on analyzing patterns of handwriting and using the information collected to verify signatures and detect fraud. Samples of handwriting were taken from three different people with two of them further asked to forge the signature of the third subject. Various image enhancing programs such as MATLAB, ImageJ and Photoshop CS3 were used to bring out the patterns of certain letters and tendencies of the writers. The results were that it was possible to use patterns and tendencies found in casual handwriting and apply them to signatures for verification.

3 1. INTRODUCTION In today s day and age, signatures and identification are needed almost everywhere. To purchase items from a store with a credit card, a signature is needed to complete the process. In order to rent anything from anywhere, almost always a signature or proof of identification is taken. The great demand and necessity for signatures and IDs has made the act of forgery a frequently performed crime. Most of the time, the easiest way to tell the difference between a authentic and a forged signature would be to look at it. By looking at two signatures, one would seem to be less professional. But, what happens when you compare two signatures which happen to look exactly the same at a superficial glance? This research was focused on handwriting analysis and signature verification. Experiments in the past have been conducted in the field of handwriting analysis such as creating mathematical models to fit a certain person s writing style. Also, studies have been done to discover the statistics behind signatures and using that information to distinguish an authentic signature to a forged one. This project used data from both regular handwriting and signatures to detect fraud. Tendencies, smears, and spots/dots were all found while analyzing handwriting from various people. The genuine signature from the first sample, as seen in Figure 1, has the same frequencies and tendencies of certain letters as the handwriting of the same person. 1

4 Figure 1: Comparison of the letter y The data can also be observed in a histogram or graph function. When the pixels in an image are put into a histogram, they should look close if not exactly the same each time. This is because the histogram picks up the number of pixels of a certain color. If the same pen and material is used, the histogram will show consistency. The purpose of this experiment was to find out whether certain people follow a pattern when writing casually and if this pattern is evident in their signature. Combining data collected from the handwriting and the signature, this study can further help detect forgery. Even though some people have the ability to forge signatures almost perfectly it is still extremely difficult to master the casual handwriting of anyone. 2. BACKGROUND For many years there has been research done in forgery and handwriting analysis. Some researchers have stated that handwriting does not exist simply because it is the brain 2

5 which signals us to make certain strokes on paper [1]. Computer technology today has allowed researchers to analyze signatures in a mathematical way. Studies focusing on the statistical part of observing forgery are becoming more common in detecting skilled forgery [3]. The amount of time put into finding mathematical models is tremendous, as can be seen in examples in Figure 2. Figure 2: Mathematical Formulas [3] Producing formulas requires extensive observation in various programs to develop graphs of different points on the signature. Also, between 20 to 50 samples are taken into consideration to test the conclusions derived from the statistical information. There are many devices available which can help verify differences between two different signatures or handwriting samples. 3. METHODS Instead of focusing on the statistical part, this experiment was more intent on finding patterns and tendencies of a persons handwriting. Sample paragraphs were collected 3

6 from a website [2] and three people were asked to produce a handwriting sample. One of these three people signed their original signature and the other two were asked to forge this signature. So, in total, there were three different handwriting samples, one authentic signature, and two forged signatures [Figure 3]. Figure 3-1: Sample Authentic Signature Figure 3-2: Sample Forged Signature These samples were observed under a microscope camera in search of patterns of letters, pen marks, or anything which distinguished that specific style of writing from another. Pictures of the same letter in different words were taken to show the consistency of the writer with certain letters. Once the data was put into a file, the images were opened within ImageJ to further enhance them and to make graphs to distinguish the signatures. In ImageJ, histograms were made of a particular point of each signature to show the differences in pixels. To get the entire signatures and not just a portion of it, every 4

7 image was scanned and inserted into Photoshop CS3 for resizing. The resized images were then opened in ImageJ and the image calculator tool was used to layer the images on top of each other to demonstrate the differences in strokes of the authentic signature and the forged one, as shown in Figure 4. The difference option was chosen so that both images would layer on top of each other and both signatures would be visible in the same image. Figure 4: Image Calculator tool 4. RESULTS The microscopic pictures taken by the ProScope HR showed patterns in the three samples of handwriting. The writer of each sample had a certain way of writing a particular letter which was the same in most cases. For example, Figure 5 shows the pattern of the letter e written by the same person in different words [Figure 5]. Every e was filled in regardless of the letter before and after it. 5

8 Figure 5: Patterns of the letter e Also, one of the samples of the forged signature was very obvious to distinguish because the writer was very meticulous with each stroke. An ink pen was used in this experiment because it helped the slow movements of the hand very easy to detect on paper. Usually, a person s signature is a smooth and fine stoke with minimal stops, as seen in Figure 6, but, in Figure 7 the writer took their time hence the thickness of the writing. Figure 6-1: Authentic Figure 6-2: Forged Sample Other patterns were also found such as the capital letter N in sample three [Figure 7]. This is a very unique type of writing which was only found in this sample. Figure 7: Patterns of the letter N 6

9 The superimposed images also showed differences between the two signatures. They showed the tendencies of the writers and when the forged and authentic signatures were put in layers, it can clearly be seen that they show no resemblance. The histograms taken of the portion of the signatures also showed that there is a difference regarding the pixels in the image. For example, there are changes in the histogram of the original, shown in Figure 8, such as the maximum and minimum pixels, which are different from the ones in the forged image as shown in Figure 9. Figure 8: Authentic Sample Histogram Figure 9: Forged Sample Histogram 5. CONCLUSIONS This research involved the analysis of handwriting to find patterns which could help verify signatures. The process required several visual image processing programs and a microscope camera to take pictures of the samples. The microscopic pictures helped point out the smears, spots of ink, tendencies, strokes, and other marks which would be difficult to see with the naked eye. The programs helped enhance these images and further brought out the differences between the signatures. Margins of error included the 7

10 type of pen, paper, and environment in which the writer is in. If a pencil or ball point pen was used instead of an ink pen, the results would have been different. This is because the ink pen better shows the strokes of the writer. It clearly shows any change of direction. Also, if a person is in the car while writing, there might be an occurrence of unwanted marks. This would make the results inaccurate. This experiment did not focus on gaining a mathematical model to the strokes and tendencies of people s signatures, but more on how the handwriting patterns are present in signatures. Acquiring this knowledge can help verify signatures and slow down forgery. 6. FUTURE WORK In the future, the idea of matching handwriting patterns to signatures will be taken to a further level. A handwriting and signature analyzing program will be developed and available to companies. The program would contain sample handwritings and signatures of everyone associated in the business. It would use the basic idea of finding stroke frequencies and tendencies of the hand to distinguish a certain person s handwriting style. The ultimate result of this project would be instant detection of forgery to prevent fraud. REFERENCES [1] Lucas, Joe, [2] World Wide Fund for Nature, [3] B. Fang, C.H. Leung;, Y.Y. Tang, K.W. Tse, P.C.K. Kwok, Y.K. Wong, Offline signature verification by the tracking of feature and stroke positions, The Journal of the Patterns Recognition Society,