Kalyan S. Dash|Niladri B. Puhan|Ganapati Panda
Handwritten numeral recognition using non?redundant Stockwell transform and bio?inspired optimal zoning
Indian Institute of Technology Bhubaneswar|Indian Institute of Technology Bhubaneswar|Indian Institute of Technology Bhubaneswar
iet image processingvolume 9 issue 10 p 874882 research articlesfree access handwritten numeral recognition using nonredundant stockwell transform bioinspired optimal zoning kalyan sourav dash corresponding author kalyan sourav dash email protected school electrical sciences indian institute technology bhubaneswar orissa 751 013 indiasearch papers authorniladri b puhan niladri b puhan school electrical sciences indian institute technology bhubaneswar orissa 751 013 indiasearch papers authorganapati panda ganapati panda school electrical sciences indian institute technology bhubaneswar orissa 751 013 indiasearch papers author kalyan sourav dash corresponding author kalyan sourav dash email protected school electrical sciences indian institute technology bhubaneswar orissa 751 013 indiasearch papers authorniladri b puhan niladri b puhan school electrical sciences indian institute technology bhubaneswar orissa 751 013 indiasearch papers authorganapati panda ganapati panda school electrical sciences indian institute technology bhubaneswar orissa 751 013 indiasearch papers author first published 01 october 2015 httpsdoiorg101049ietipr20150146citations 23aboutsectionspdf toolsrequest permissionexport citationadd favoritestrack citation shareshare give accessshare full text accessshare fulltext accessplease review terms conditions use check box share fulltext version articlei read accept wiley online library terms conditions useshareable linkuse link share fulltext version article friends colleagues learn morecopy url abstract handwritten digit recognition one challenging problems character recognition large variation writing styles individuals presence similar looking shapes different numerals feature extraction techniques based statistical topological attributes image spatial domain barring works attempting feature extraction transformed domain another challenge optimal selection zones extracting features localised zones unknown test image cases recognition phase isolated training phase makes impossible adaptively improve feature selection using knowledge obtained error analysis study authors propose feature extraction technique new character recognition problem using nonredundant stockwell transform another transformed domain feature extraction using slantlet coefficients proposed also propose use bioinspired evolutionary computingbased optimisation techniques adaptively select optimal zone arrangement feature selection stage knowledge classification accuracy proposed methods experimentally validated handwritten digit database odia language proves outperform recognition accuracy reported 1 introduction optical character recognition ocr art machine recognition scripts script printed handwritten acquired either online offline recognition irrespective nature script acquisition method type language belongs ocr system follows standard procedures preprocessing feature extraction classification postprocessing recognition machine printed scripts relatively easier standard text alignment shapes obvious reason real challenge faced research community recognition handwritten script handwritten digit recognition subtask broad handwritten character recognition problem finds specific application postal automation finding zip codes bank cheque processing form data entry extraction feature distinguishing descriptors vital stage recognition system selection feature must facilitate maximisation interclass pattern variability minimisation intraclass variance order achieve high accuracy recognition problem becomes acute presence similar looking images different classes variation writing style individuals accumulation noise data acquisition leads additional challenge fig 1 techniques extracting features offline scanned images categorised two groups namely spatial domain feature extraction transformed domain feature extraction spatial domain features based statistical properties image structural characteristics combination 15 however considering spatial domain attributes signal miss important feature frequency fig 1open figure viewerpowerpoint handwriting samples odia numerals isi kolkata database lookalike shapes similar lowfrequency components lowfrequency components reflect basic shape character whereas detailed variations found highfrequency region property inspired many researchers extract image features fourier transform ft 68 ft coefficients twodimensional 2d image considered set feature recognition researchers also obtained topological description object first generated features using 1d ft known fourier descriptors however frequency information obtained fourier coefficients global miss localised variations shape handwriting technique feature extraction using ft provided good results expected printed ocr however failed cases unconstrained handwritten documents alleviate limitation ft gabor wavelet basis functions become popular accomplish localised frequency analysis multiresolution framework feature extraction using gabor transform 914 wavelet transform 1518 hough transform 19 reported literature unlike ft commonly used orthonormal wavelet representations fail analyse phase information signal phase wavelet transform relative centre analysing wavelet wavelets translate reference points phases translate therefore absolutely referenced phase information character image obtained stockwell proposed timefrequency representation signal 1996 known stockwell transform st stransform 20 uniquely combines progressive resolution absolutely referenced phase information st proved perform superior transforms many applications image texture characterisation 21 image compression 22 palm image classification 23 geophysical applications 24 one drawback st redundancy spacefrequency decomposed representation consumes computing well storage resources example st applied n n image results 4d complex value representation order computation storage on4 n4 logn on4 respectively limitation prevents use st images experiment high spatial resolution requiring extensive processing time well gigantic storage disk therefore computationally efficient orthonormal version st proposed 25 accounts nonredundancy representation paper propose new effective zonebased feature extraction technique using nonredundant st nrst handwritten digit recognition train recognise characters using features extracted employing st nearest neighbour nn classifier chosen classification error generated used adaptively change zone arrangements hence problem considered optimisation problem bioinspired techniques 2629 genetic algorithm ga particle swarm optimisation pso differential evolution de used optimally select zones fig 2 validate performance proposed methods handwritten numerals odia language odia one popular languages indian subcontinent spoken 44 million people one classical languages recognised indian government rich history 3000 years fig 2open figure viewerpowerpoint proposed paradigm adaptive zoning feature extraction using bioinspired techniques remainder paper organised follows section 2 explains basics st well orthonormal representation section 3 describes optimisation techniques adopted zone selection section 4 feature extraction using zonebased nrst znrst outlined along adaptive zone selection strategy using bioinspired techniques based recognition error minimisation slantlet transform similar approach feature extraction using slantlet coefficients combined optimised zoning reported section 5 experimental results indian statistical institute isi kolkata database 30 well newly developed odia handwritten digit database iit bhubaneswar using fixed znrst adaptive znrst slantlet transform obtained simulation study presented section 6 finally conclusions discussion results investigation summarised section 7 2 st nonredundancy st gives full temporal frequency spatial frequency decomposition signal 2d ft hkx ky signal hx defined 1where x spatial variables kx ky represent frequency variables respective directions definitions inverse 2d ft 2the 2d st defined 2d gaussian modulation cosinusoidal functions gaussian kernel changes shape spatial frequencies kx ky 3as local spectral analysis provided st generalisation global fourier spectrum integration sx kx ky spatial variables equals 2d ft hkx ky 4alternatively st also expressed progressive spectral shifting operation fourier spectrum 5where dimension frequencies kx 0 ky 0 extending 3 discrete signal n image st obtained 6here p 0 1 n 1 tx sampling interval along p direction q 0 1 1 ty sampling interval along q direction resulting st matrix thus 4d complex variable matrix cannot visualised planner space compact representation computationally efficient storagewise effective means n image transformed n spatial frequency decomposition requires orthonormal transformation involves calculating basis functions derived taking linear combinations fourier complex sinusoids band limited subspaces applying appropriate frequency phase shifts adopt octave sampling rule creating orthonormal basis functions centres spatial frequency bands widths defined two variables fx fy 7the nrst n image thus calculated partitioning 2d ft image performing inverse 2d ft appropriate phase correction normalisation factor ensure validity energy conservation theorem vector norms 8here define horizontal vertical voice frequencies n normalisation term square root number points partition whereas discrete wavelet transform dwt 15 gives horizontal vertical diagonal detail coefficients order decomposition nrst gives information voice frequencies n contain bandwidth frequencies 25 3 adaptive zone selection optimisation character recognition system feature extraction classification two important stages popular successful approach feature extraction irregular shapes case unconstrained handwritten alphanumerals spatial regional decomposition images form small zones zone separately contributes formation feature vector traditional zoning methods summarised 31 found 3239 idea conventional zoning superimpose regular rectangular grids pattern images fig 3 fig 3open figure viewerpowerpoint different zone arrangements feature extraction pattern images existing literature 3139 inherent problem fixed zone arrangements take account lies within zones contributes recognition accuracy isolation zone selection therefore feature extraction classification goes unchecked propose use bioinspired optimisation techniques reformulate problem ocr optimal choice problem choice effectively use zone arrangement character database minimise recognition error hence cost function optimisation accuracy error objective maximise minimise respectively depending one choose zone contributes feature vector features extracted nrst slantlet transform initial zone arrangement evaluates initial cost function used train feature extraction module adapt zones process iterates till optimal zoning obtained resulting minimal error considered ga pso de separately designing adaptive featureclassifier model algorithm details basic algorithm steps ga de pso found 2629 40 4 proposed adaptive zone nrst aznrst feature extraction propose aznrst feature extraction algorithm two approaches 41 approach one adaptive zone 2d nrst zone subimage first approach propose involves computation 2d orthogonal st images numeral image whose feature vector extracted read recognition system generally scanned greylevel image sometimes red green blue image input image greynormalised mean intensity 0 maximum intensity value 1 compute minimum bounding box exactly holds character region interest image binarised using otsus global thresholding method 41 connected component analysis performed single connected component rectangle fitting character consideration computed minimum bounding box image size normalised standard size images used training recognition size divide image nine zones division performed follows consider set nine points image plane xi yi 1 2 9 hereafter known zone pointers points corresponds row column locations pixels locations image set nine distance measures computed nine selected locations pixel belong particular zone distance pixel xi yi minimum 9for creating pool initial population make small perturbations locations zone pointers two random numbers generated pointer one row one column subtract 05 make perturbation move direction x image plane multiplication factor 3 multiplication factor obtained experimentation ensure initial randomisation pointer locations fall range 15 15 process repeated l number times obtain initial population size l set l 10 zone optimisation problem proposed algorithm steps outlined fig 4 fig 4open figure viewerpowerpoint steps proposed algorithm l different zone arrangements calculate 2d nrst nine zones stockwell coefficients thus obtained complex numbers take absolute value magnitude coefficients nine zones concatenate form feature vector size 9 7 7 441 every individual initial population proceed compute feature vectors training images apply nn classifier recognise unknown numerals distribution training test images database 8020 ratio total 5000 images handwritten odia numeral database choose 4000 images training 1000 testing comprising ten numeral classes noted training images different images used testing validation moreover single zone arrangement applies images training test images regardless digit class particular class could bias recognition results favour optimisation process takes care exhaustive search zone pointer locations ensures adaptation process fall local minima cost function evaluated 1000 test image results used optimise zone arrangement subsequently trains different set 4000 images feature extraction therefore set images trained optimal zone arrangement arrangement applied unknown image recognition minimal error 42 approach two adaptive zone 1d nrst contour descriptors second approach combines structural feature information well spectral information obtain feature vector initial zone pointers defined per fig 4 stage instead taking 2d nrst individual zones image propose compute chord distribution edge points numeral particular zone respect zone belong edge contour greynormalised minimum bounded image computed uniform sampling performed obtain fixed number contour points points lie one nine zones defined zone pointers direction distance contour point corresponding zone pointer evaluated gives two sets 1d feature distributions terms distances angles compute 1d nrst two distributions zone take absolute values resulting complex coefficients concatenate form feature vector fig 5 fig 5open figure viewerpowerpoint chord distribution contour points respect zone pointers one generation obtain set classification results depending initial zone pointer positions follow procedure updating population zone pointer positions different set contour descriptors obtained contour points might shifting different zone respective parent zones new sets distance direction profiles transformed 1d nrst representations following similar steps compute features optimisation problem reiterates till reaches optimal zone arrangement corresponding highest classification accuracy 5 slantlet transformbased feature extraction exploring subsequently exploiting ability decompose image orthogonal spacefrequency representation st intuitive venture possibilities transformed domain feature extraction approaches slantlet transform proposed selesnick 1999 42 proved give good outcomes 1d data compression power quality events 43 image watermarking 44 brain image classification 45 multispectral image fusion 46 many times surpassing performance wavelets therefore motivated take advantage slantlet transform extracting features handwritten numeral recognition slantlet special case class basis functions similar discrete wavelets derived gramschmidt orthogonalisation 43 slantlet transform orthogonal dwt two zero moments improved scalespace localisation example stockwell slantlet transforms two numeral images displayed fig 6 slantlet filters described following set equations 10 fig 6open figure viewerpowerpoint original image left st centre slantlet transform right odia numerals 1 5 proposed feature extraction using slantlet transform follows similar steps znrst fig 4 fixed rectangular zones updated using optimisation techniques reduce classification error 6 experimental results tenfold crossvalidation procedure followed experimentation parameters chosen listed table 1 obtained via experimentation validate proposed algorithm existing handwritten odia numeral database isi kolkata 30 well new database created us iit bhubaneswar fig 7 noted although odia one six classical widely used languages india previously benchmarking database handwritten digit recognition except one developed isi kolkata 30 table 1 parameters taken simulation validating proposed algorithm classify test images population l initial zone pointer perturbation ga mutation probability de control factor f pso inertia weight w pso random weights r1 r2 pso behaviour parameters c1 c2 10 3 04 15 2 0 1 0 2 fig 7open figure viewerpowerpoint sample handwritten digits iit bhubaneswar database new database handwritings collected diverse group individuals children age group 1215 individuals age groups 1625 midage adults sexagenarians include contributors rural urban demography pangender different literacy levels samples collected different geographic regions different climatic conditions individuals asked write unconstrained environment different font sizes shape irregularities captured collected documents scanned 300 600 dpi database available publicly request research purposes although process creation database ongoing affair validation algorithm proposed paper considered 500 different images numeral class making total size database 5000 sample confusion matrix 1000 test images provided table 2 nrst features using fixed zone arrangements table 3 reports results using proposed optimised feature extraction technique approach 2 de test images recognition results 1000 test images using slantlet features fixed adaptive zone arrangements given similarly tables 4 5 respectively performance comparison recognition accuracies variants proposed methods namely three optimisations two transform domain feature extractions 1d 2d variants presented fig 8 tenfold crossvalidated recognition rates well recognition times per test image best variant proposed method isi kolkata iit bhubaneswar database summarised table 6 superiority proposed algorithm existing odia handwritten numeral recognition techniques isi kolkata database reported table 7 table 2 confusion matrix 1000 test images using fixed zone nrst feature 0 1 2 3 4 5 6 7 8 9 99 1 0 0 1 0 0 0 0 0 0 98 0 0 0 0 0 0 1 1 0 0 96 1 0 0 5 3 0 0 0 0 0 99 0 0 0 0 0 0 1 0 0 0 99 1 0 0 0 0 0 0 0 0 0 99 0 0 0 0 0 0 1 0 0 0 92 3 0 0 0 0 3 0 0 0 3 93 0 0 0 1 0 0 0 0 0 0 98 2 0 0 0 0 0 0 0 0 1 97 table 3 confusion matrix 1000 test images using adaptive zone 1d nrst feature 0 1 2 3 4 5 6 7 8 9 100 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 2 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 98 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 100 table 4 confusion matrix 1000 test images using fixed zone slantlet feature 0 1 2 3 4 5 6 7 8 9 98 0 0 0 0 0 0 1 0 0 0 92 0 0 0 1 0 1 0 1 0 0 95 0 0 0 2 10 0 0 0 0 0 98 0 0 0 0 0 0 0 1 0 0 97 8 0 0 0 1 0 0 0 0 0 86 0 0 0 0 0 0 3 1 0 1 97 8 0 1 0 0 2 0 0 0 1 80 0 0 2 7 0 1 0 1 0 0 100 1 0 0 0 0 3 3 0 0 0 96 table 5 confusion matrix 1000 test images using adaptive zone slantlet feature 0 1 2 3 4 5 6 7 8 9 99 0 0 0 1 0 0 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 98 0 0 0 0 2 0 0 0 0 1 100 0 0 0 0 0 0 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 99 0 0 0 0 0 0 0 0 0 0 100 3 0 1 0 0 1 0 0 0 0 95 0 0 1 0 0 0 0 0 0 0 100 1 0 1 0 0 0 0 0 0 0 98 table 6 tenfold crossvalidation recognition rate recognition time using adaptive zone 1d nrst de method resulted highest accuracy isi kolkata iit bhubaneswar database database method set 1 set 2 set 3 set 4 set 5 set 6 set 7 set 8 set 9 set 10 recognition rate isi kolkata adaptive zone 1d nrst de 993 998 991 989 989 991 985 99 994 99 iit bhubaneswar 988 994 988 986 983 991 983 981 979 986 recognition time per test image ms isi kolkata adaptive zone 1d nrst de 6145 6133 6171 615 6136 6157 6161 6136 6147 6134 iit bhubaneswar 6031 606 5985 5972 5982 6036 5995 5956 5876 5917 table 7 performance comparison proposed method existing techniques ascending order accuracy method feature classifier accuracy isi kolkata database iit bhubaneswar database bhowmik et al 48 scalar hmm 905 na roy et al 49 directional quadratic 9481 na proposed method fixed zone slantlet knn 952 921 proposed method fixed zone 2d nrst knn 96 958 proposed method adaptive zone slantlet ga knn 984 967 pal et al 47 directional mqdf 984 na dash et al 39 hybrid topological features dlqdf 985 na proposed method adaptive zone 2d nrst de knn 988 984 proposed method adaptive zone 1d nrst de knn 991 986 fig 8open figure viewerpowerpoint performance comparison recognition accuracies variants proposed methods overall recognition rates isi kolkata iit bhubaneswar database b recognition time per test image isi kolkata iit bhubaneswar database intel i7 32 bit 2 gb ram machine clearly proposed feature extraction technique considering image transformation along optimising zone arrangements proved perform better previous methods previous reported methods simulated along proposed ones compare recognition times none papers explicitly mentioned time intel i7 32 bit processor 2 gb random access memory ram average recognition time using methods 39 47 5138 5594 ms respectively compared 6147 ms taken proposed method adopted knn classifier statistical classifier model classifying unknown input vector basically consists finding k similar vectors given training set identifying class frequently matched among k neighbours therefore intrinsically knn classifiers zero training time requirement store whole training set priori perform classification based closest training samples feature space 7 conclusion paper presented novel feature extraction technique using nrst well using slantlet transform proposed formulation character recognition system optimisation task resulted choosing optimal zone arrangement minimum classification error proposed technique dual paradigms one taking 2d nrst zone subimages computing contour descriptors reference zone pointers taking 1d nrst distance direction profiles contour points employed three bioinspired heuristic optimisation tools namely ga de pso make use error analysis order update zone characteristics extract robust features pertaining local variations handwritings highest accuracy observed debased zone optimisation technique along 1d nrst contour descriptors 991 also evaluated performances proposed feature extraction methods classifiers support vector machine quadratic discriminant classifier neural networks although recognition accuracy using classifier models reach range 9598