634350765869323109seminartongquanocr

8/3/2019 634350765869323109SeminarTongQuanOCR

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I HC DUY TNKHOA CNG NGH THNG TIN

Ch

NHN DNG CHV

CC HNG NGHIN CU

Ngi trnh byTS. PHM ANH PHNG

B mn C s Tin hc

Friday, March 04, 2011


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GII THIU


Nhn dng ch l mt lnh vc c quan tm nghin cu vng dng tnhiu nm nay theo hai hng chnh:

Nhn dng ch vit tay: vi nhng mc rng buckhc nhau v cch vit, kiu ch... phc v cho cc ng dng

c v x l cc chng t, ha n, phiu ghi, bn vit taychng trnh... Nhn dng ch vit tay c tch ra haihng pht trin: nhn dng ch vit tay trc tuyn (on-line) v chvit tay ngoi tuyn (off-line).

Nhn dng ch in: phc v cho cng vic t ng ha cti liu, tng tc v cht lng nhp thng tin vo my

tnh trc tip tcc ngun ti liu.


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GII THIU (tt)


Nhn dng chvit tay: vn cn l vn thch thc ln

i vi cc nh nghin cu. Bi ton ny cha thgii quyttrn vn c v n hon ton ph thuc vo ngi vit vs bin i qu a dng trong cch vit v trng thi sckhe, tinh thn ca tng ngi vit.

Nhn dng ch in: c gii quyt gn nh trn vn(sn phm FineReader 9.0 ca hng ABBYY c thnhn dngch in theo 192 ngn ng khc nhau, phn mm nhn dngchVit in VnDOCR 4.0 ca Vin Cng ngh Thng tin H Ni

c th nhn dng c cc ti liu cha hnh nh, bng vvn bn vi chnh xc trn 98%).


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LCH SPHT TRIN


Cc sn phm nhn dng ch thng mi c t nhng nm1950, khi my tnh ln u tin c gii thiu tnh nng mi

v nhp v lu trd liu hai chiu bng cy bt vit trn mttm bng cm ng. Cng ngh mi ny cho php cc nhnghin cu lm vic trn cc bi ton nhn dng ch vit tayon-line.

Nhn dng ch c bit n t nm 1900, khi nh khoa hcngi Nga Tyuring pht trin mt phng tin tr gip cho

nhng ngi m.

Giai on 1: (1900 1980)


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LCH SPHT TRIN


Nm 1954, my nhn dng ch u tin c pht trin biJ. Rainbow dng c ch in hoa nhng rt chm.

M hnh nhn dng chvit c xut tnm 1951 do phtminh ca M. Sheppard c gi l GISMO, mt robot c-vit.

Giai on 1: (1900 1980) (tt)

Nm 1967, Cng ty IBM thng mi ha h thng nhndng ch.


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LCH SPHT TRIN


Cc hng tip cn theo cu trc v i snh c p dngtrong nhiu h thng nhn dng ch.

Vi s pht trin ca cc thit b phn cng my tnh v ccthit b thu thu nhn d liu, cc phng php lun nhn dng c pht trin trong giai on trc c c mi trng

l tng trin khai cc ng dng nhn dng ch.

Giai on 2: (1980 1990)

Trong giai on ny, cc hng nghin cu ch tp trung vocc k thut nhn dng hnh dng chcha p dng cho thngtin ngngha. iu ny dn n shn chv hiu sut nhn

dng, khng hiu qu trong nhiu ng dng thc t.


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LCH SPHT TRIN


Cc k thut nhn dng kt hp vi cc phng php luntrong lnh vc hc my (Machine Learning) c p dngrt hiu qu.

Cc h thng nhn dng thi gian thc c ch trng tronggiai on ny.

Giai on 3: (T1990 n nay)

Mt s cng c hc my hiu qu nh mng n ron, m hnhMarkov n, SVM (Support Vector Machines) vx l ngnngtnhin...


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Giai on tin xl


Nh phn ha nh Lc nhiu Tm xng

Hiu chnh nghing


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Giai on tch ch


Tch dng Tch t, k t


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CC PHNG PHP TRCH CHN C TRNG

Bin i ton cc v khai trin chui

c trng thng k

c trng hnh hc v hnh thi


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c trng thng k

Phn vng (Zone) Cc giao im v khong cch


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c trng thng k (tt)

Chu tuyn (Contour Profile) Projection histograms


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c trng thng k (tt)

c trng hng (Direction Features)

Cc k t c m t nhcc vectm cc phn tca n l cc gi trthng k vhng.


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c trng hnh hc v hnh thi

Cc cu trc hnh thi: da trn cc cu trc nguyn thy(on thng, cung) to ra k t.

Cci lng hnh hc: cc k tc biu din bng oca cc i lng hnh hc nh t sgia chiu rng v chiu caoca hp cha k t, quan h khong cch gia hai im, so snh di gia hai nt, rng ca mt nt, khi lng ch hoa vchthng ca cc t, di t...

thv cy: u tin, cc t hoc cc k tc phn chiathnh mt tp cc i tng nguyn thy nh cc nt, cc imchc... Sau , cc thnh phn nguyn thy c s dng trongcc th lin quan.


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CC HNG TIP CN NHN DNG


i snh mu

Tip cn cu trc

Cu trc ngphp(Grammatical Methods)

Phng php th

(Graphical Methods)

Ch p dng tt i vi nhn

dng ch in, cn chvit tay tht ra km hiu qu.

S dng trong giai on hu

x l sa cc li m khinhn dng thc hin sai

Ch vit c m t bi cc th, mi th l skt hp ca

cc dng nguyn thu: onthng, cung


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CC HNG TIP CN NHN DNG (tt)


Tip cn thng k da trn c s ba gi thuyt chnh:

1. Phn b ca tp c trng l phn b Gauss hoctrong trng hp xu nht l phn b u.

2. C cc s liu thng k y c th dng chomi lp.

3. Tp nh {I} c th trch chn mt tp c trng

{fi}F, i{1,...,n} m tp c trng ny i dincho mi lp mu ring bit.

k-NNk-NN BayesBayes


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Cc phng php hc my tin tin

M hnh Markov n

(HMM Hidden MarkovModel)

M hnh Markov n

(HMM Hidden MarkovModel)

Mng n ron

(NN - Neural Network)

Mng n ron

(NN - Neural Network)

My vect ta

(SVM - Support Vector Machines)

My vect ta

(SVM - Support Vector Machines)


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Kt hp cc chin lc nhn dng

Mi k thut phn lpu c nhng u im

v nhc im ring.

Kt hp vi nhau theo mtcch no nng cao

hiu qu nhn dng

Xy dng cc kin trckt hp phn lp

Kin trc tun tKin trc tun t

Kin trc song songKin trc song song

Kin trc lai ghpKin trc lai ghp


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Kin trc tun tKin trc tun t

Kin trc song songKin trc song song

Kin trc lai ghpKin trc lai ghp

Chuyn kt qu u ra ca mt my

phn lp thnh u vo ca myphn lp tip theo, cc chin lctiu biu: Boosting, thc nc

Kt ni kt qu ca cc myphn lp c lp ca nhiuchin lc khc nhau. Tiu biu

nht l chin lc b phiu vlut quyt nh Bayes

Lai ghp gia hai kin trc tun t

v song song.


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CC KIN THC CN THIT NGHIN CU


X l nh (Image Processing)

Hc my (Machine Learning)

Xc sut thng k v ton ng dng

Ngn nghc v ngn nghc tnh ton

(Linguistic and Computational Linguistic)

Mng n ron, HMMSVM

Boosting,..

Kernel method

Bayes

k-NN,..

n-Gram

http://www.kernel-machines.org/


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CC B DLIU CHUN PHC V NGHIN CU


B dliu USPS (United States Postal Service)

B dliu MNIST (National Institute of Standardand Technology of the United States)

gm 7291 mu dng Train v 2007 mu khc test,mi mu l mt nh a cp xm kch thc 1616.

gm 60.000 mu dng Train v 10.000 mu khc test,mi mu l mt nh a cp xm kch thc 2828.


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MT S KT QU THC NGHIM TRN TP MNIST


CLASSIFIER ERROR (%) Reference

K-nearest-neighbors, L3 1.22 Kenneth Wilder, U. Chicago

K-NN, shape context matching 0.63 Belongie et al. IEEE PAMI 2002

K-NN with non-linear deformation

(P2DHMDM)0.52 Keysers et al. IEEE PAMI 2007

SVM deg 4 polynomial 1.1 LeCun et al. 1998

Reduced Set SVM deg 5 polynomial 1.0 LeCun et al. 1998

Virtual SVM deg-9 poly [distortions] 0.8 LeCun et al. 1998

Trainable feature extractor + SVMs 0.54 Lauer et al., Pattern Recognition 40-6, 2007

3-layer NN, 500+300 HU 1.53 Hinton, unpublished, 2005

2-layer NN, 800 HU, MSE 0.9 Simard et al., ICDAR 2003

2-layer NN, 800 HU, cross-entropy 0.7 Simard et al., ICDAR 2003

NN, 784-500-500-2000-30 + nearest neighbor,

RBM + NCA training1.0 Salakhutdinov and Hinton, AI-Stats 2007

http://yann.lecun.com/exdb/mnist/


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KT LUN

Nhn dng ch in c gii quyt gn nhtrn vn Nhn dng chvit tay (online/Offline) vn l bi ton m

Trong nc: cc gii php nhn dng chvit tay tingVit vn ang c quan tm, nghin cu.

Xu hng s dng cc kin trc lai ghp gia cc

phng php nhn dng, Boosting tng tc cngnh chnh xc nhn dng.

M hnh ngn ng thng k N-Gram trong giai on hu

x l cng l ch rt ng quan tm.


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KT LUN (tt)

Pht trin cc ng dng nhn dng trn cc Form chvit tay


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Cm nquv ch lng nghe!

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