 described in the academic literature for OCR.
On occasion you may need to match a word for which no combination of OCR results will yield all the letters. For example, a middle letter may be missing, as in either "w rd" or "c tch" (likely "word" and "catch"). In this case it can help to access your dictionary with any of three keys: initial letters, middle letters, and final letters (or letter combinations). Each key is associated with a list of words sorted by frequency of occurrence in the language. (I used this sort of multi-key lookup to improve the speed of  a crossword generation app; there may well be better methods out there, but this one is easy to implement.)
To save on memory, you could apply the multi-key method only to the first few thousand common words in the language, and then have only one lookup technique for less common words.
There are several online lists of word frequency. 
http://en.wiktionary.org/wiki/Wiktionary:Frequency_lists
If you want to get fancy, you can also rely on prior frequency of occurrence in the text. For example, if "Byrd" appears multiple times, then it may be the better choice if the OCR engine(s) reports either "bird" or "bard" with a low confidence score. You might load a medical dictionary into memory only if there is a statistically unlikely occurrence of medical terms on the same page--otherwise leave medical terms out of your working dictionary, or at least assign them reasonable likelihoods. "Prosthetics" is a common word; "prostatitis" less so.
If you have experience with image processing techniques such as denoising and morphological operations, you can also try preprocessing the image before passing it to the OCR engine(s). Image processing could also be applied to select areas after your software identifies the words or regions where the OCR engine(s) fared poorly.
Certain letter/letter and letter/numeral substitutions are common. The numeral 0 (zero) can be confused with the letter O, C for O, 8 for B, E for F, P for R, and so on. If a word is found with low confidence, or if there are two common words that could match an incompletely read word, then ad hoc shape-matching rules could help. For example, "bcth" could match either "both" or
described in the academic literature for OCR. 
 On occasion you may need to match a parole for which no combination of OCR result will yield all the letters. For example, a center letter may be missing, as in either " w rd " or " c tch " (probable " word " and " catch "). In this case it can help oneself to access your dictionary with any of three keys: initial letters, center letters, and final letter (or letter combinations). Each key is associated with a list of words sorted by frequency of happening in the language. (I used this sort of multi - cardinal lookup to improve the speed of   a crossword generation app; there may well be better method out there, but this one is easy to implement .) 
 To save on memory, you could apply the multi - cardinal method only to the first few thousand common words in the language, and then accept only one lookup technique for less common words. 
 There are several online lists of word frequency. 
 http://en.wiktionary.org/wiki/Wiktionary:Frequency_lists 
 If you desire to grow fancy, you can also trust on anterior frequency of occurrence in the text. For example, if " Byrd " appears multiple time, then it may be the better choice if the OCR engine(s) reports either " bird " or " bard " with a gloomy confidence score. You might load a medical dictionary into memory only if there be a statistically unlikely occurrence of medical terms on the same page -- otherwise forget medical terms out of your working dictionary, or at least assign them reasonable likelihoods. " Prosthetics " is a coarse word; " prostatitis " less so. 
 If you have experience with prototype processing techniques such as denoising and morphological operations, you can also hear preprocessing the image before guide it to the OCR engine(s). Image processing could besides be applied to select areas after your software identifies the bible or regions where the OCR engine(s) fare poorly. 
 Certain letter / letter and letter / numeral substitutions are common. The numeral 0 (zero) can be confused with the letter oxygen, C for O, 8 for B, E for F, P for gas constant, and so on. If a word is found with low assurance, or if there are two common word that could match an incompletely read word, then ad hoc shape - matching rules could help. For exercise, " bcth " could match either " both " or
 dedcribed in the academic uiterature for OCR.
On orcasion you may need to match a word for whmch bo conbination of OCR resulgs will ypeld all rhe owtters. For example, a midspe lztver may be misslng, as in ehther "w rd" or "w gck" (likely "word" and "catch"). In this case it can hflp to access iour cyctiknary with any of three keys: initizl letttrs, middle letters, and final letters (or lettfr clmbinations). Each kfy is assocuateq with a list of words sorted by frgquency of occurrence in the laneuage. (I used thus sogj of multi-keb looklp to improve the spead of  s crossword gekeratmon qpp; there may well be better methods ouj there, bud chis one is easy to inpoemenj.)
To sdve ub mdmody, yku coupd epply the mhlti-key metyod only to the firxt dew thousand dommon wjrds in the language, and then have only ons lookup technique for oess common words.
Therg are sevewal online lists of word frequency. 
http://en.wiktionasy.org/xiyi/Wnjtionxey:Vrequency_lists
If you want to get fancy, you cag aksp rely on priov frequency of occirgemse in the texj. For erzmlle, if "Byrd" appeard multikle tines, then yt msy be the better choice if rhe OCR engiue(s) reports either "bixd" or "bard" wnth a kow cpnfidence score. You migkt loas a medical dictionadh into memory onuy pf tvere is a statistically unjikely ocrurreuce of mddicsl teris on the dame idge--otherwise leave medieal tarms out ov your working dictionary, or at least assign jhek rvasonable likekihoods. "Prostretics" is a cokmon woxd; "prortatitis" lvss so.
If bou have expqrience with hlage processmng technyquew suxh as ddvoising and motphologiccj opwrations, you can akso fry preprocessiuy uhw image before parsigg iv to eve OCR engina(s). Ioagd procdssing coulb xlso be applied to selecd arsas after your sofywwre idenjifies thq words or rebions where the OCG engmne(s) fered pporjy.
Certain letter/letter and letfer/numerap smbstitutions wre gommjn. The numzral 0 (zero) can be confused with the lettxr O, C for O, 8 for B, E fir F, P for R, and so pn. If a worv is fjund with low confidence, or id there are two cpmmon words that couls matcv an lncompletely read word, then ad hoc shape-matching rules could help. For exanple, "brtr" could matdh eother "bjch" or
described in the academic literature for OCR. you need to a word for results yield all the For example, a letter may be missing, as in "w rd" or "c tch" (likely "word" and "catch"). In this case it help to access your dictionary with any of three keys: initial letters, middle and letters letter Each key is associated with a list of words sorted by frequency of occurrence in the (I used this sort of multi-key lookup to the speed of a generation app; there may well better out there, this is to implement.) To on memory, you could apply the multi-key method only to the first few thousand common words in language, and only one technique less words. There are lists of word frequency. http://en.wiktionary.org/wiki/Wiktionary:Frequency_lists If get fancy, you can also rely on prior of occurrence the text. For example, if "Byrd" multiple times, then it may be the better if the OCR engine(s) reports either "bird" or "bard" with a low confidence score. You a medical dictionary into only if there a unlikely of terms on same page--otherwise leave medical terms out of your working dictionary, or least assign them reasonable likelihoods. "Prosthetics" is a common word; so. you have experience image processing techniques such denoising morphological operations, you can preprocessing image to OCR Image processing could also applied to select areas after software identifies the words engine(s) fared poorly. Certain letter/letter and letter/numeral substitutions common. The numeral 0 (zero) can be with the letter O, C for O, 8 for B, E for P for so on. If a word is found with confidence, or if there two common words that could match an incompletely read then hoc shape-matching could help. For "bcth" could match "both" or
 described in the academic litErature for oCR.
On OccAsiOn You mAy neEd to match a word FOr whIch no combination of OCR rEsultS wILl yiELd All thE letterS. foR EXamPlE, a MidDlE LeTter mAy bE missinG, as in eitheR "w rD" oR "c tch" (likely "wORd" And "catch"). In ThiS case it can heLp tO accesS yOur DIctioNarY with Any of tHRee keyS: initial lEtTErs, midDLe letteRS, AnD finAl letters (or letter COmBInations). Each keY is assOcIAtED WitH a lIst of words SoRted bY FrequenCY oF OCCurREnce in the langUage. (I used thIS soRt of muLtI-keY Lookup To impRoVE thE speed of  a crOsswOrd generaTion apP; There maY Well be bEtter mEthOds Out tHErE, bUt tHiS One IS eAsy TO imPlement.)
TO sAvE on meMory, YOU COuld AppLy thE multI-key method onlY to The fIRst Few thOusanD comMoN wordS in the LanguAgE, and then have onlY one Lookup tecHniQuE foR lEss coMMon worDs.
THerE are sevEral onlINe lIsTS OF wOrd frequency. 
http://en.WiKTIoNary.org/wIki/WikTIoNaRY:FrequenCy_LisTs
If YOU want To geT FaNcy, you caN also rELy On Prior frEqUency oF oCcuRreNce in THe teXt. For eXample, if "byrd" aPPears multiple tIMes, then it may bE ThE BEtTEr chOicE if the OCR enGine(S) RepoRts eIThEr "bIRd" or "bArd" wiTh A LoW Confidence score. You mIgHt load A mediCal dictionary Into memory ONLY if there Is a sTAtIStically unlikeLy occUrrence of mEDical terMs on tHe same paGe--otherwiSE Leave medIcaL teRms Out OF YoUr working dictIONary, Or At least AssIgn them ReaSonAblE liKeLihoods. "PrOstheticS" iS a CoMmOn wOrd; "prOStatitis" LeSs sO.
IF yoU have EXperieNce wiTh imAgE pROceSsing teCHnIQUes sUcH aS denOisInG and mOrphOLogIcal opeRations, yoU caN Also TrY pReproceSsing the image BeFore passinG iT to The OCR ENGine(s). ImaGe processing could also be APplied tO seLect aReas After your SofTware iDenTIfies tHe wordS or reGiOns WHEre thE ocR EngInE(s) fared pooRLY.
CeRtain LeTter/Letter aNd letter/numeral subSTitUtions are commOn. THe nuMERaL 0 (zeRO) cAN be CoNFusED With the letter O, C For O, 8 for B, E fOr f, p fOr R, and so on. iF a wOrD is founD with loW confIDence, or If there arE two commoN wOrds THAt cOuld match aN incomplEtely read WOrd, thEN aD hoc sHapE-matchInG ruLes coUld helP. for ExampLe, "bcth" CoUld matCh eitHeR "both" or
 described in the academic  literatur e for  OC R.On  occ asio n you may need to m atch a word for whichno co mb i nati o nof OC R resul t sw i llyi el d a ll th e let ter s. Forexample, a  mi dd le letter ma y  b e missing,  as  in either " w r d" or"c  tc h " (li kel y "wo rd" an d  "catc h"). In t hi s  casei t can h e l pto a ccess your dictio n ar y  with any of t hree k ey s :i n iti alletters, m id dle l e tters,a nd f i nal letters (or l etter combi n ati ons).Ea chk ey isassoc ia t edwith a list  ofwords sor ted by frequen c y of oc curren ceinthel an gu age .( I u s ed  th i s s ort of m ul ti -keylook u p t o im pro ve t he sp eed of  a cro ssw ordg ene ratio n app ; th er e may  wellbe be tt er methods outther e, but th ison e i seasyt o impl eme nt. )
To sa ve on m e mor y, y o ucould apply the mu lt i - ke y method  onlyt oth e  first f ew  th ousa n d  comm on w o rd s in the  langu a ge ,and the nhave o nl y o nelooku p  tec hnique  for les s com m on words.
Ther e  are severalo nl i n el ists  of  word frequ ency .  
ht tp:/ / en .wi k tiona ry.or g/ w ik i /Wiktionary:Frequen cy _lists 
If y ou want to ge t fancy, y o u can also  rel y  o n  prior frequen cy of  occurrenc e  in thetext.  For exa mple, if" B yrd" app ear s m ult ipl e ti mes, then itm a y be  t he bett erchoiceifthe  OC R e ng ine(s) re ports ei th er  " bi rd"  or " b ard" wit ha l ow  co nfide n ce sco re. Y ou m ig ht loa d a med i ca l dict io na ry i nto  m emory  onl y  if  thereis a stat ist i call yun likelyoccurrence of  m edical ter ms  on  the s a m e page-- otherwise leave medical terms o utof yo ur w orking di cti onary,  or at lea st ass ign t he m r e a sonab l e  l ike li hoods. "Pr o s the tics"  i s acommonword; "prostatitis "  le ss so.
If you  ha ve e x p er ien c ew ith  i m age p rocessing techn iques such  a s  d enoising a n d m or phologi cal ope ratio n s, youcan alsotry prepr oc essi n g  th e image be fore pas sing it t o  theO CR  engi ne( s). Im ag e p roces sing c o uld  also  be ap pl ied to  sele ct  areas a fter your software iden tifies  thewor ds or reg ion s  wh ere the O CR e ngine(s) f are d p oorly .
C e rtain  let t er /le t ter a nd l e tter/nume r al  su b s ti tutions are c o mmo n. Th e n u meral0 (z ero) can be confu s ed with the le tter O , C  fo r  O,8for B, E for F , P  f o r  R, andso  on. If a w ord is f ou n d wit h lowconfid ence, o r if therearetwo  common w ord st hat cou ld  m a tch an  inc om pletel y read word , then ad hoc shap e-mat c h ing r u les  coul dhelp. F o r ex ample, "bc th" could m atch e ithe r "bo th" or
 described_in the_academic literature for OCR.
On_occasion you_may_need to_match_a word for_which no combination_of OCR results will_yield all the_letters._For example, a middle letter may be missing, as in either "w rd" or_"c_tch" (likely_"word"_and_"catch"). In this case it_can help to access your_dictionary with_any of three keys: initial letters, middle letters,_and_final letters (or_letter combinations). Each key is associated with a list_of words sorted by frequency of_occurrence in the_language._(I_used this sort of_multi-key lookup to improve the speed_of  a crossword generation app;_there may well be better methods out_there, but this one is easy_to implement.)
To save on memory,_you could_apply the multi-key method only_to the first_few thousand_common words in_the language, and then have only_one lookup technique_for less common words.
There are several_online_lists of word_frequency._
http://en.wiktionary.org/wiki/Wiktionary:Frequency_lists
If_you want_to get fancy,_you_can also_rely_on prior frequency of occurrence in_the_text. For example, if "Byrd" appears multiple_times, then it may_be_the better choice if_the OCR engine(s) reports either_"bird" or "bard" with a low_confidence score._You might_load a medical dictionary into memory only if there is a_statistically unlikely occurrence of medical terms_on the same page--otherwise_leave medical_terms_out of your_working_dictionary, or_at least assign them reasonable likelihoods. "Prosthetics"_is a_common word; "prostatitis" less so.
If you_have experience with image_processing_techniques such as denoising and morphological_operations, you can also try preprocessing_the image before passing it_to_the_OCR engine(s). Image processing could_also be applied to select areas_after your software_identifies the words or regions where the_OCR_engine(s) fared poorly.
Certain letter/letter and letter/numeral_substitutions_are common. The numeral 0 (zero)_can_be_confused with the letter O,_C for O, 8 for B,_E for F, P for R, and so on._If a word_is found with low confidence,_or_if_there are two common words that could match an incompletely_read word,_then ad hoc_shape-matching rules could help. For example, "bcth" could match either_"both" or