Neural Network Applications Neural Network Applications in OCRin OCR

Daniel Hentschel

Robert JohnstonCenter for Imaging Science

Rochester Institute of Technology

BackgroundBackground

Recently, many Ancient documents have been discovered. Deciphering these documents is integral to understanding our past and predicting our future as a human race.

BackgroundBackground

Unfortunately, the majority of ancient Hebrew documents found are seriously degraded.

BackgroundBackground

This degradation makes deciphering of the documents difficult. Many techniques have been developed to enhance the text in degraded manuscripts in order to improve readability.

The majority of this work has been carried out by Roger Easton and Robert Johnston at the Center for Imaging Science at RIT.

Note:

Question:Question:

Would it be beneficial to use a neural network to help in the deciphering of degraded ancient Hebrew documents?

Research PurposeResearch PurposeCreate an application to assist in

document restoration.Develop a neural network adept

at Hebrew OCR (optical character recognition).

Analyze the functionality of the network when studying degraded characters

Creating an ApplicationCreating an Application

Developed with Visual BasicDeveloped with Visual Basic

Calculate Input FeaturesCalculate Input FeaturesSegmentation.Horizontal and Vertical section averages.Length of the skeleton as a percentage of

the circumscribed rectangle perimeter.Complexity: Square root of the black area

divided by the length of the skeleton.Number of dead ends and intersections in

the skeleton of the character.

SegmentationSegmentation

Simple segmentation. Not elegant, but functional.

Thinning AlgorithmThinning Algorithm

Adapted from “A Fast Thinning Algorithm For Characters” (Flores, Rezende, Carrijo, Yabu-tti)

Red - pixel being analyzed

Green/Blue - to be deleted

Workings of a Neural NetworkWorkings of a Neural Network

Input LayerHidden Layer

Output Layer

Nodes in a Neural NetworkNodes in a Neural Network

Inputs are multiplied by a weighting factor and a bias is added.

Weighted inputs are summed.Sum is applied to a function:

11

2Output

Input Weighted

e

Internal Function of a NodeInternal Function of a NodeNode Function

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-10 -8 -6 -4 -2 0 2 4 6 8 10

Input

Ou

tpu

t

Operation of a NodeOperation of a Node

Input to the Neural NetworkInput to the Neural Network9 input nodes

– Horizontal (3) and Vertical (2) histogram.– Length of the skeleton as a percentage of the

circumscribed rectangle perimeter.– Complexity: Square root of the black area divided

by the length of the skeleton.– Number of changes of intersections, and dead

ends encountered in the skeleton.

Output of the Neural NetworkOutput of the Neural Network

4 output nodes

Hidden NodesHidden Nodes

3 hidden nodes works well for 4 characters

ResultsResults

Training set:

ResultsResults

Characters not in training set:.07

.00

.94

.04

.05

.05

.92

.05

.08

.04

.03

.94

.07

.95

.02

.02

.86

.05

.05

.02

.08

.04

.04

.92

.12

.77

.01

.13

ConclusionConclusion

The neural network is quite effective at deciphering non-degraded text.

Not enough degraded characters have been studied yet to determine how well the network will perform

Future work:– More than 4 characters– Optimize inputs– Analyze degraded characters

The EndThe End