Perhaps the following modules, together with a dictionary and a few lines of Perl will do?

• String::Approx - Perl extension for approximate matching (fuzzy matching)
• Text::Levenshtein - An implementation of the Levenshtein edit distance
• Text::Soundex - Implementation of the soundex algorithm. (for names)

Except that you probably shouldn't use Text::Soundex for almost anything. It is a hashing technique for matching names that follow common patterns for English given names and surnames. It works by dropping various letters, combining various letter sequences and truncating the result. It is an interesting but fairly blunt instrument good for grouping names or adding "see also" entries in a telephone book in England and a few other English speaking countries, but much less useful for any other purpose.

True laziness is hard work

"I assume that the length of the word remains unchanged in the scrambled form."

Even if your "same length" criteria is true, then based of the number of matching characters -- sk0oid .v. should -- there are 543 words in my dictionary that could be considered matches for sk0oid:

c:\test>junk33 sk0oid | find "33.33%" | wc -l
    543
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These range from:

c:\test>junk33 sk0oid | find "33.33%"
sk0oid has a 33.33% chance of being: abroad
sk0oid has a 33.33% chance of being: acarid
sk0oid has a 33.33% chance of being: accord
...
sk0oid has a 33.33% chance of being: shoots
sk0oid has a 33.33% chance of being: shored
sk0oid has a 33.33% chance of being: should
sk0oid has a 33.33% chance of being: shoved
sk0oid has a 33.33% chance of being: showed
sk0oid has a 33.33% chance of being: shrewd
sk0oid has a 33.33% chance of being: shroff
sk0oid has a 33.33% chance of being: shrove
sk0oid has a 33.33% chance of being: shuted
...
sk0oid has a 33.33% chance of being: uphold
sk0oid has a 33.33% chance of being: upload
sk0oid has a 33.33% chance of being: verbid
sk0oid has a 33.33% chance of being: vespid
sk0oid has a 33.33% chance of being: vetoed
sk0oid has a 33.33% chance of being: viscid
sk0oid has a 33.33% chance of being: zeroed
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You would need to come up with a heuristic that reflects the types of mistakes that your OCR program has a habit of making in order to get anything like good results.

The program used above:

#! perl -slw
use strict;

open W, '<', 'words.txt' or die $!;
my @words = <W>;
close W;
chomp @words;

chomp( my $bad = lc shift() );

for my $good ( @words ) {
    next if length $good != length $bad;
    my $mask = $good ^ $bad;
    my $match = $mask =~ tr[\0][];
    next unless $match;
    printf "$bad has a %.2f%% chance of being: $good \n", 
        $match / length( $good ) * 100;
}
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I think is a questionable technique, you could change completely the meaning of the text without notice unless you add some people to the equation and check line by line (and even in this case checking should be much more easier before with the original OCR!).

If this is not possible in any case, maybe you could improve the method if you repeat the OCR several times and do the match several times in different copies of the same page

and/or

extract a dictionary with all the words used in your files, count the words, sort the similar words by probability of appearing in the file and thus do the replace taking in mind this. Forget the idea of a complete dictionary with a lot or very rare words. Is much more error prone.

Depending on your font and how drunk you are, the letters "b", "h", and "k" might look alike. So remap them all to the letter "b". Do this for any other look-alikes.

This is sort of like Text::Soundex, except with the look instead of sound of a word.

#!/usr/bin/perl

use warnings;
use strict;

my %dict;
@ARGV = 'words.txt';

while (<>) {
    chomp;
    my $word = $_;
    tr/gq9xz2mwbhk68ilj1acenosu05/gggzzzmmbbbbbiiiiaaaaaaaaa/;
    $dict{$_} .= "$word ";
}

while (<DATA>) {
    chomp;
    my $word = $_;
    tr/gq9xz2mwbhk68ilj1acenosu05/gggzzzmmbbbbbiiiiaaaaaaaaa/;
    my $matches = $dict{$_} || '~';
    print "$word: $matches\n";
}

__END__
sk0oid
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