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

More documents

Recommendations

Info

provides a variety of approaches to solvethe problem of multi-class classification [21].Most of the existing multi-class algorithmsaddress the problem by first dividing it intosmaller sets of a pair of classes and thencombine the results of binary classifiersusing a suitable voting methods such asmajority or weighted majority approaches[21], [22].Fig. 5: A rooted binary decision directed acyclic graph (DDAG) multi-class classifier for eight-class classificationproblem.5.1 Multi-class SVMsMulti-class SVMs are usuallyimplemented as combinations of two-classsolution using majority voting methods. Ithas been shown that the integration ofpairwise classifiers using decision directedacyclic graph (DDAG) results in betterperformance as compared to other populartechniques such as decision tree, Bayesian,etc. [19].We construct a decision directed acyclicgraph (DDAG), where each node in thegraph corresponds to a two-class classifierfor a pair of classes. The multi-classclassifier built using decision directedacyclic graph (DDAG) for 8-classclassification problem is shown in Fig. 5. Itcan be observed that the number of binaryclassifiers built for a N class classificationproblem is N ( N −1) / 2.The input vector ispresented at the root node of the DDAGand moves through the DDAG until itreaches the leaf node where the output(class label) is obtained. At each node adecision is made concerning to which classthe input belongs.For example, at the root node of Fig. 5,a decision is made whether the inputbelongs to class 1 or class 8. If it doesnotbelong to class 1, it moves to the left child;otherwise, if it doesnot belong to class 8, itmoves to the right child. This process isrepeated at each of the remaining nodes tillit reaches the leaf node where the decisionis reached to classify the input as one of theeight classes.
VI. RESULTS AND DISCUSSIONSWe have a recognition system thatconverts a given document images intoequivalent textual format. The systemaccepts either already scanned documentimages or scan a given Amharic textdocument at a resolution of 300 dpi on aflat-bed scanner, HP7670 Scanjet scanner.The scanned document is binarized, noiseremoved, skew corrected and scaled beforeindividual components are extracted.Preprocessed pages are then segmentedinto character components for featureextraction and classification. FollowingAmharic letters shape formation; we canfirst decompose lines in a text page intowords and then words into appropriatecharacter components for recognition andthen recompose the recognizedcomponents to determine the order ofcharacters, words and lines in a text page.Once character components areidentified, optimal discriminant features (ina lower dimensionality space) are extractedfor classification. We use a two stagedimensionality reduction scheme based on99 percent principal component analysiswhich is followed by 15 percent lineardiscriminant analysis.The original dimension of feature vectorof each character image (afternormalization) is 400 ( 20× 20 ). Principalcomponent analysis reduces thedimensionality from 400 to 295 and lineardiscriminant analysis further to 50. We aredealing with such reduced optimaldiscriminant feature vectors. Both methodsperform well in feature dimensionalityreductions. The use of a two-stage featureextraction scheme further solves thesimilarity problem encountered during theapplication of PCA alone. This is becauseof the fact that the new scheme extractsoptimal features that discriminate betweena pair of characters employed forclassification using support vector machine.We conduct extensive experiments toevaluate the performance of the recognitionprocess on the various datasets of Amharicscripts. The experiments are organized in asystematic manner considering the varioussituations encountered in real-life printeddocuments. Our datasets are of two types:One set of datasets considers printingvariations (such as fonts, styles and sizes).The other is degraded documents such asnewspapers, magazines and books. Wereport the performance of the recognizer inall these datasets and the result obtained ispromising to extend it for other indigenousAfrican scripts.ResultPowerGeezFonts Point Size StyleVisualAgafari Alpas 10 12 14 16 Normal Bold ItalicGeezDatasets 7850 7850 7850 7850 7680 7680 7680 7680 7680 7680 7680Accuracy 99.08 96.24 95.53 95.16 98.64 99.08 98.06 98.21 99.08 98.21 89.67Table 2: Performance result of the Amharic OCR on pages that varies in fonts, sizes and styles.In the first experiment, we consider theprinting variation. We test on the mostpopular fonts such as PowerGeez,VisualGeez, Agafari and Alpas that areused for typing and printing purposes, fourpoint sizes (10, 12, 14 and 16) and fontstyles (such as normal, bold and italics).Performance results are shown in Table 2.
Page 1 and 2: Optical Character Recognition of Am
Page 3: offices in the form of corresponden
Page 7 and 8: different representations of the ch
Page 9: ynandmanner.ycnnc n= L= sare determ

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?