# Mathematical Handwriting Recognition with a Neural Network and Calculation

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• Topic: Image scanner, Optical character recognition, Neural network
• Pages : 9 (2844 words )
• Published : February 23, 2013

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Mathematical Handwriting Recognition with a Neural Network and Calculation Author: Tyler Sondag Date: 4/22/07 For Dr. Pokorny's CSI 490 Course

Abstract The goal of this project was to create a software system that recognizes handwritten mathematical expressions and computes the answer. No special syntax or formatting was to be required for these expressions, since a major goal of this system was for users to be able to use the system without having to learn anything new. Support was desired for algebraic expressions, integrals, and summations. The Java programming language was chosen for this project because of its ability to be used on a number of different operating systems and architectures without recompiling the source code. A graphical front end was also desired in order for the system to be more user friendly.

Table of Contents 1. Introduction....................................................................................................................................3 2. Images............................................................................................................................................4 3. Neural Network..............................................................................................................................7 4. Scanner...........................................................................................................................................9 5. Parser............................................................................................................................................10 6. GUI...............................................................................................................................................11 7. Future Work.................................................................................................................................11 8. Conclusion...................................................................................................................................12 References..........................................................................................................................................13

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1. Introduction Handwriting recognition is done in two different ways. The first is on-line recognition which examines the characters as the user is drawing them. This method is the simpler of the two, since the system only deals with one character at a time. An example of this method is character recognition on a personal digital assistant (PDA). The second type is off-line recognition. In off-line recognition the system must look at an entire group of characters instead of just one at a time. An example of this is optical character recognition (OCR) software for scanners. This system will use off-line character recognition. Once the system has broken a picture into its individual characters, a neural network will be used to determine each individual character. Next these characters, as well as information regarding their locations, are sent to the scanner. The scanner then rebuilds the individual characters into numbers and also determines which symbol goes to the parser next. In some cases, the scanner must also insert additional characters. The parser then requests one character at a time from the scanner and calculates the expression. Finally, a pop-up is displayed with the calculated answer.

Figure 1: Example

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2. Images In this system, images are able to be input in two different ways. In either case, images are required to be gray scale. Support may eventually be added for non-gray scale images, but this was not considered important for the initial version of the system. The first method of picture input is with a bitmap file. The functionality for loading bitmap files was included for several reasons. First, since bitmap files do not compress the picture data no external libraries were required. Thus, converting the file into a data...