Development of an OCR SystemThird Quarter

Nathan HarmataPeriod 5

Recap of Goals for 3rd Quarter

More heuristics for Character Recognition

Make results more “spread out”Make results more “spread out”

Minor goal: “generic character models”

Diagram of OCR System

Input Image

Blocks of Text

LinesLines Lines

Words WordsWords

ImageProcessing

CharacterRecognition

LetterLetter Letter

Transformations Transformations

Comparison to GCDD and recognition

CharacterModel

Image Processing

Uses whitespace between lines and words

Character Recognition

Developed new heuristic: GapVector

Developed GCD (Generic Character Definitions)

Character Models and Attributes

Attribute

My two comparison heuristics, SectorVector and GapVector, are extensions of the Attribute class

Attribute

Description ComparisonScalar

Method of comparison

Method of output to database

Uses specific comparisonused by the overriding class

Uses outputs of overriding class

Data

Character Model

Came up with idea during 2nd Quarter

Way of organizing data and making code cleaner

Character Model

Attribute Attribute HashMap of Attributes

Method of comparison

Method of output to database

Uses vector difference of Attribute Vectors

Uses outputs of Attributes

Method of hashing Uses hashcode of output

Sector Vector - SectorParsing

Deals with the major flaw with SlopeField

Parses the image into portions that pass the vertical line test. Each portion is then transformed into a SlopeField.

Sector Vector -SlopeField

Gap Vector -Theory

Separates the different letters into groups

Examples: n, m u, v Q, o, 0

Gap Vector -Gap Parsing

First find the four corners of the letter

Defined as the intersection of the two paths

Gap Vector -Gap Parsing

First idea: relative gap coverages-coverage of pixels on the line that would exists if there were a gap

Straight-line pathAreas of pixel coverage

Gap Vector -Gap Parsing

Second idea: locations of pixels relative to the gap

This just ends up being a comparison of the area of the pixels in front of the straight line to the areas of the pixels behind the straight line

-More pixels in front of gap -> no gap

Gap Vector -Gap Parsing

Putting everything together

Example Character Model output:

c SectorVector -2 3 GapVector R

Do this for every letter of the alphabet for a lot of different fonts and average the results

To recognize an individual image of a letter, find the best matches in the cache based using the “compareTo” method of the CharacterModel class

Character Model Cache

a SectorVector -5 5 GapVector b GapVector SectorVector 4 3 c SectorVector -2 3 GapVector R d SectorVector -1 3 GapVector e SectorVector -2 3 GapVector f SectorVector 0 3 GapVector R g SectorVector -1 5 GapVector h SectorVector 0 1 GapVector i SectorVector 0 2 GapVector L j SectorVector 0 4 GapVector k SectorVector -2 3 GapVector R l SectorVector 0 1 GapVector m SectorVector -3 1 GapVector T n SectorVector -1 1 GapVector o SectorVector -3 3 GapVector p GapVector SectorVector 4 3 q SectorVector -1 3 GapVector r SectorVector 0 1 GapVector R s SectorVector -2 6 GapVector t SectorVector 0 3 GapVector u SectorVector 0 1 GapVector T v GapVector T SectorVector -2 1 w SectorVector -5 1 GapVector T x SectorVector -4 3 GapVector T L y SectorVector -2 3 GapVector T L z SectorVector 1 4 GapVector L

** Use Java Reflections for generic Attribute handling

Goals for 4th Quarter

Get everything working together-almost done, haven’t tested it yet

Think of another heuristic if the results are good enough

Make GUI for OCR system

Noise removal