Neural Network based Handwriting Recognition

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    19-Feb-2016
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Neural Network based Handwriting Recognition. Presented By Lingzhou Lu & Ziliang Jiao. Domain. Optical Character Recogntion (OCR) Upper-case letters only. Motivation. Build our own handwriting recognition system that can recognition a simple sentence or phrase. Problems. - PowerPoint PPT Presentation

Transcript of Neural Network based Handwriting Recognition

Neural Network based Handwriting Recognition

Neural Network based Handwriting RecognitionPresented ByLingzhou Lu & Ziliang Jiao

Domain Optical Character Recogntion (OCR) Upper-case letters onlyMotivation Build our own handwriting recognition system that can recognition a simple sentence or phraseProblemsEach person has an unique writing styleWritten characters varies in sizes, stroke, thickness, styleGeneralization of the recognition system largely depends on the size of training set

Traditional algorithmic approach follows a set of instructions to solve problems.

ApproachImage AcquisitionPre-processingSegmentationFeature ExtractionClassification & RecognitionThere are 4 major stages for handwriting recognition Unipen datasetsContains 16414 samples of isolated upper case lettersAround 600 full sets of 26 alphabetic lettersOnly 300 are used70% training set10% validating set20% testing setProblemsMissed labeled Mixed with Cursive dataUnreadable data

Problematic cases in UnipenPre-processingUsing Aforge libraryMinimize the variability of handwritten character with different stroke thickness, color, and size Convert to binary imageCropped ImageSkeletonization

Feature ExtractionExtracting from the raw data the information which is most relevant for classification purposes.

Every character image of size 90x60 is divided into 54 equal zones, each of size 10x10 pixels

ExperiementNeural Network using back propagationNetwork parametersANN representation: 69-100-26Activation function: Hyperbolic tangent/Sigmoid Training epochs: 10000Learning Rate: 0.0005Momentum Rate: 0.90Terminated condition: validation set MSE

n= input units * output units

ExperiementDistortionSimilar to mutation in GA0.01 possibility at every epoch Every image has 50% chance to be distorted

Example of distortionResult DistortionTraining SetTesting setYES92%83%NO97%75%

No distortion => converge fasterConclusion Distortion helps generalize recognition system Better result can be yield with larger training Validation set can be use to avoid overfitting and find the best generalized result

Application

Implement an additional spellchecker module at the output to prevent misrecognize from similar characters

Future Work Expand dataset Look for better segmentation and feature extraction method Apply GA to feature input to find out the possible better solution