Optical Character Recognition of Amharic Documents - CVIT - IIIT ...

different representations of the characters.Some of these features consider profiles,structural descriptors and transform domainrepresentations [2], [16]. Alternates onecould consider the entire image as thefeature. The former methods are highlylanguage specific and become verycomplex to represent all the characters inthe script. The later scheme providesexcellent results for printed characterrecognition.As a result, we extract features from theentire image by concatenating all the rowsto form a single contiguous vector. Thisfeature vector consists of zeros (0s) andones (1s) representing background andforeground pixels in the image, respectively.With such a representation, memory andcomputational requirements are veryintensive for languages like Amharic thathave large number of characters in thewriting.Therefore we need to transform thefeatures to obtain a lower dimensionalrepresentation. There are various methodsemployed in pattern recognition literaturesfor reducing the dimension of featurevectors [17]. We consider in the presentwork Principal Component Analysis (PCA)and Linear Discriminant Analysis (LDA).4.1 Principal Component AnalysisPrincipal component analysis (PCA) canhelp identify new features (in a lowerdimensional subspace) that are most usefulfor representation [17]. This should be donewithout losing valuable information.Principal components can give superiorperformance for font-independent OCRs,easy adaptation across languages, andscope for extension to handwrittendocuments.Consider thethi image samplerepresented as an M dimensional (column)vector xi, where M depends on the imagesize. From the large sets of trainingdatasets, x , , x Nwe compute thecovariance matrix as:1∑ =NN∑i 11 L T( xi− µ )( xi− µ )= (1)Then we need to identify minimal dimension,say K such that:K∑ − 1λ ∑i i/ Trace()thi≤ α (2)where λiis the largest eigenvalue of thecovariance matrix ∑ and α is a limitingvalue in percent.Eigenvectors corresponding to thelargest K eigenvalues are the direction ofthgreatest variance. The k eigenvector isthe direction of greatest variationstperpendicular to the first through ( k −1)eigenvectors. The eigenvectors arearranged as rows in matrix A and thistransformation matrix is used to computethe new feature vector by projecting asY = . With this we get the best oneiAx idimensional representation of thecomponent images with reduced featuresize.Principal component analysis (PCA)yields projection directions that maximizethe total scatter across all classes. Inchoosing the projection which maximizestotal scatter, PCA retains not only betweenclassscatter that is useful for classification,but also within-class scatter that isunwanted information for classificationpurposes. Much of the variation seenamong document images is due to printingvariations and degradations. If PCA isapplied on such images, the transformation