1. 1 General Background: The human brain can easily recognize the number "2" in hundreds of different sizes and fonts. Computers, however, aren't as smart as people. The problem is that scanners produce bitmap images, which look like the one below. Word processors are not capable of editing bitmap images. So how do you convert the scanned images of your term paper into something that you can edit with a word processor? Bitmap image of the number "2" as produced by a scanner.

The OCR software is designed just for this task. OCR software does this in three primary methods: Pattern Matching, Feature Extraction and Spell Checking. 1. 2 Objective: The objective of this project is to scan guided printed and handwritten Arabic numerals, then recognize them. The recognition process is done by using feature extraction method.

1. 3 Project Outline: Step 1: having a document of printed or handwritten Arabic numerals. Step 2: Scan it, then save it as a monochrome bitmap file. Step 3: the program reads in the bmp file and produce the character matrix, which contains the bits representation of the image. The white pixels are represented by 1 s and the black pixels by 0 s. Then the program extracts the real matrix from the character matrix by determine the smallest rectangle that contains the number.

Step 4: Recognize the number, which is represented by the real matrix, according to a decision tree of features. Depending on the features we move downward until we reach the leave, which is the expected number. 1. 1 General Background: The human brain can easily recognize the number "2" in hundreds of different sizes and fonts.

Computers, however, aren't as smart as people. The problem is that scanners produce bitmap images, which look like the one below. Word processors are not capable of editing bitmap images. So how do you convert the scanned images of your term paper into something that you can edit with a word processor? Bitmap image of the number "2" as produced by a scanner. The OCR software is designed just for this task. OCR software does this in three primary methods: Pattern Matching, Feature Extraction and Spell Checking.

1. 2 Objective: The objective of this project is to scan guided printed and handwritten Arabic numerals, then recognize them. The recognition process is done by using feature extraction method. 1. 3 Project Outline: Step 1: having a document of printed or handwritten Arabic numerals.

Step 2: Scan it, then save it as a monochrome bitmap file. Step 3: the program reads in the bmp file and produce the character matrix, which contains the bits representation of the image. The white pixels are represented by 1 s and the black pixels by 0 s. Then the program extracts the real matrix from the character matrix by determine the smallest rectangle that contains the number.

Step 4: Recognize the number, which is represented by the real matrix, according to a decision tree of features. Depending on the features we move downward until we reach the leave, which is the expected number. 1. 1 General Background: The human brain can easily recognize the number "2" in hundreds of different sizes and fonts. Computers, however, aren't as smart as people. The problem is that scanners produce bitmap images, which look like the one below.

Word processors are not capable of editing bitmap images. So how do you convert the scanned images of your term paper into something that you can edit with a word processor? Bitmap image of the number "2" as produced by a scanner. The OCR software is designed just for this task. OCR software does this in three primary methods: Pattern Matching, Feature Extraction and Spell Checking. 1. 2 Objective: The objective of this project is to scan guided printed and handwritten Arabic numerals, then recognize them.

The recognition process is done by using feature extraction method. 1. 3 Project Outline: Step 1: having a document of printed or handwritten Arabic numerals. Step 2: Scan it, then save it as a monochrome bitmap file.

Step 3: the program reads in the bmp file and produce the character matrix, which contains the bits representation of the image. The white pixels are represented by 1 s and the black pixels by 0 s. Then the program extracts the real matrix from the character matrix by determine the smallest rectangle that contains the number. Step 4: Recognize the number, which is represented by the real matrix, according to a decision tree of features. Depending on the features we move downward until we reach the leave, which is the expected number.