I'm creating a program that reads a scanned hand written document and coverts it to text. The recognized words must come from a dictionary of about 300 words that I create. As an example, if the hand written word is recognized as "heilo", but my dictionary only contains "hello" and "world", it should convert it to "hello". However, if it recognized it as "planet", it shouldn't match it to anything. I think a possible approach would be to create a score of how closely the recognized word matches each word in the dictionary. If it doesn't get a minimum score, then no match is found.
I'm writing the application in C#. Are there any libraries/examples available that be do something like this, or would I have to code everything from scratch?
Thanks
There is nothing in the standard libraries to compute the distance between words, but there are plenty of examples you can find on the internet: look up "edit distance" or "Levenshtein distance". The idea is to measure the similarity in terms of the number of changes to the first string in order to make it a second string. The distance between "heil" and "hello" is 2, because you need to replace "i" with "l" (first edit), and then append an "o" (the second edit).
When looking for an implementation or implementing your own, avoid the trivial implementation with a 2D array, because it's not memory-efficient. Use the modification with O(min(m,n)) memory requirements instead of the "naive" O(m*n).
I have no lib at hand to do what you need but searching the web knowing that you want to calculate the Levenshtein Distance might help you in your search.
Perhaps you should start with a spell checker - there are a number of libraries available that do this.
There are a few c# snippets online that will get the ball rolling:
Levenshtein:
http://www.dotnetperls.com/levenshtein
Boyer-Moore:
http://www-igm.univ-mlv.fr/~lecroq/string/node15.html#SECTION00150
Based on those, you can easily implement your own Word Matcher module.
I have a xml with two properties: word and link.
How can I replace the words on a text to a link using the xml information.
Ex.:
XML
<word>dog</word>
<link>http://www.dog.com</link>
Text: The dog is nice.
Result: The dog is nice.
Results OK.
The problems:
1- If the text has the word dogs the result is incorret, because of "s".
2- I've tested doing a split by space on text to fix it, but if the word is composed like new year the result is incorret again.
Does anyone have any suggestions to do it and fix these problems (plural and compound words)?
Thanks for the help.
You can use Lucene.Net's contrib package Snowball for stemming (words->word , came->come , having->have etc.). But you will still have troubles with compound words
If you roll your own solution, I have had good success with the .NET pluralization capabilities:
http://msdn.microsoft.com/en-us/library/system.data.entity.design.pluralizationservices.pluralizationservice.aspx
Essentially, you can pass a word in its plural form and receive a singular version and vice versa.
This could be fairly intensive depending on how often the content changed, i.e. this wouldn't be a good choice to search thousands of words in real time.
Assuming that you can pre-process/cache the results or that the source file is small, you could:
Run Once
Identify all candidate words from the source file.
Parse/split phrases and pass them through the pluralization libraries to determine their plural counterparts.
Generate (and precompile) simple regular expressions to locate the words that you do want to match. For example, if you want to match "dog" but not "dogs" you could create a regex like dog[^s] which could then be executed against the text.
Run Whenever a Search/Replace is Needed
Run your list of source expressions against the text in question. I would suggest ordering the expressions from shortest to longest (otherwise a short expression may replace a word that was just parsed by a longer expression).
Again, this would be processor intensive to run in real-time (most solutions will be). As always, if you are parsing HTML, you should use an HTML parser, not a regular expression. In this case, you might use a proper parser to locate all text nodes and then perform the search/replace on them.
An alternative solution would be to put the text and keyword list into a database and use SQL Server Full Text Indexing which tends to be pretty smart about these things and supports intelligent match predicates. You could even combine this with a CLR stored procedure to handle things that .NET excels at (like string parsing).
Regardless of the approach, this will not be an exact science.
You're likely going to need a dictionary. Create a text file/XML file that contains both the singular and plural forms of the words you want. At runtime, load them into a Dictionary<String, String>. Then look up the value of <word/> in the dictionary and extract its singular value.
I'm going to write a program that takes a URL and counts the occurrences of EVERY single 1-word, 2-word, and 3-word phrases in the webpage (and possibly x-word phrases).
Here's the best algorithm I could come up with:
1). strip html tags
2) make everything lowercase
3) split the text on space and put them all into an array
4) iterate over each word, and for each word you must: put word[i], word[i+1], word[i+2] into a hashtable.
Every time u have a collision you increase the word count for that word or 2-3 letter word phrase.
My questions are:
1) Can anyone provide any more efficient solutions in terms of space and runtime?
2) Are there any easy ways to do #1 in C#?
I can probably use a dom parser and parse out all the inner text maybe.
Depending on your case, You might be oversimplifying the problem and/or You may end up putting a lot of effort implementing functionalites that already exist in some libraries. So this will not be an direct answer but suggestion on what path to take in tackling this problem.
Process You want to implement is called information retrieval. It is very broad and complex but luckily there is a lot of research in this area. Part of it is extracting word ngrams (ngram is set of consecutive letters or words in sequence).
Let me show you some additional problems you should think of ahead:
is the capitalization of letters in word important?
is dot the only sign that You want to use to mark the end of sentence?
do You want to exclude stop words? Stop words are words You don't want to include in phrase like 'a', 'the', 'I', 'my' and so on.
do you want to stem words? Convert words from their original form to their root form, like plural to singular form: basketballs -> basketball
And for extracting pure text from HTML:
extract only text shown on page?
extract hints also? (like those shown when hovering mouse over picture)
Any other non-visible text (meta tag and so on)
There are libraries that perform searching and extracting information from raw material. "Raw material" means that You have to process document (html, doc, pdf, image, ...) and turn it into text in order for search engine to index it (extract phrases, for instance). Once document is indexed it can be searched. One such library for .NET is Lucene.NET. It supports different stemmers, analyzers, filters.
I am not sure but i believe there are libraries for extracting text from html also.
Basically, your approach may work in some simpler scenarios where not so small error-level is acceptable. I recently gain interest in information retrieval and found it really complex and interesting. You may get benefits researching this topic depending on your goals. There is a lot of info here on stackoverflow as well as the rest of Internet.
And if You decide to go this way, there is much more info on Lucene (orioginal Lucene JAVA version, Lucene.NET is port to .NET) than on Lucene.NET. So if You don't find answer for Lucene.NET immediately do a search on Lucene discussions.
To answer your question #2.
HtmlDocument doc = WebBrowser1.Document;
string text = doc.GetInnerText();
If you want to make it more efficient - use a suffix trie (you may have to write your own)
http://en.wikipedia.org/wiki/Suffix_trie
A suffix trie basically makes searching through strings depend on the length of the string instead of the length of the array. Its the sort of thing they use in search engines.
I'd like to give users the ability to search through a large list of businesses, but still find near matches.
Does anyone have any recommendations on how best to go about this when you're not targeting simple dictionary words, but instead complex names like ABC Business Name?
Regards.
Check out the wikipedia article on Levenshtein distance. It's a fairly simple concept to wrap your head around and pretty easy to implement an algorithm in whichever language you are using, in your case, C#.
I found an example in C# for you here.
Also, here is an example of a spelling corrector from Peter Norvig of Google. It was said on the SO podcast a few episodes ago that Jon Skeet attempted a rewrite of this same algorithm in C#. Not sure if he completed it and/or made it publicly available though.
Consider using Keyword match and edit distance based similarity. Might combine with 'original searched' to 'actually clicked'.
This is probably a crazy solution but could you split the business name by space and then search either all the items or maybe the first couple.
So you might search on 'ABC' and 'Business' but leave out 'Name' as this might take too long.
You might even check to see if the string is of a certain length, then trim and just search on the first say 5 letters.
Have you had a look at "soundex" as a way of searching through your businesses. Again, I think you'd need to split the name by space.
You might check out the SQL Server SOUNDEX and DIFFERENCE functions. SOUNDEX converts a sequence of characters (such as a word) into a 4-character code which will be the same for similar-sounding words. DIFFERENCE gives a number which represents how "different" two strings are based on sound.
You could, for example, create a computed column based on the SOUNDEX function and match on that column later. Or you could use DIFFERENCE in a WHERE clause.
I'm trying to build an efficient string matching algorithm. This will execute in a high-volume environment, so performance is critical.
Here are my requirements:
Given a domain name, i.e. www.example.com, determine if it "matches" one in a list of entries.
Entries may be absolute matches, i.e. www.example.com.
Entries may include wildcards, i.e. *.example.com.
Wildcard entries match from the most-defined level and up. For example, *.example.com would match www.example.com, example.com, and sub.www.example.com.
Wildcard entries are not embedded, i.e. sub.*.example.com will not be an entry.
Language/environment: C# (.Net Framework 3.5)
I've considered splitting the entries (and domain lookup) into arrays, reversing the order, then iterating through the arrays. While accurate, it feels slow.
I've considered Regex, but am concerned about accurately representing the list of entries as regular expressions.
My question: what's an efficient way of finding if a string, in the form of a domain name, matches any one in a list of strings, given the description listed above?
If you're looking to roll your own, I would store the entries in a tree structure. See my answer to another SO question about spell checkers to see what I mean.
Rather than tokenize the structure by "." characters, I would just treat each entry as a full string. Any tokenized implementation would still have to do string matching on the full set of characters anyway, so you may as well do it all in one shot.
The only differences between this and a regular spell-checking tree are:
The matching needs to be done in reverse
You have to take into account the wildcards
To address point #2, you would simply check for the "*" character at the end of a test.
A quick example:
Entries:
*.fark.com
www.cnn.com
Tree:
m -> o -> c -> . -> k -> r -> a -> f -> . -> *
\
-> n -> n -> c -> . -> w -> w -> w
Checking www.blog.fark.com would involve tracing through the tree up to the first "*". Because the traversal ended on a "*", there is a match.
Checking www.cern.com would fail on the second "n" of n,n,c,...
Checking dev.www.cnn.com would also fail, since the traversal ends on a character other than "*".
I would use Regex, just make sure to have it the expression compiled once (instead of it being calculated again and again).
you don't need regexp .. just reverse all the strings,
get rid of '*', and put a flag to indicate partial match
till this point passes.
Somehow, a trie or suffix trie looks most appropriate.
If the list of domains is known at compile time, you may look at
tokenizing at '.' and using multiple gperf generated machines.
Links:
google for trie
http://marknelson.us/1996/08/01/suffix-trees/
I would use a tree structure to store the rules, where each tree node is/contains a Dictionary.
Construct the tree such that "com", "net", etc are the top level entries, "example" is in the next level, and so on. You'll want a special flag to note that the node is a wildcard.
To perform the lookup, split the string by period, and iterate backwards, navigating the tree based on the input.
This seems similar to what you say you considered, but assuming the rules don't change each run, using a cached Dictionary-based tree would be faster than a list of arrays.
Additionally, I would have to bet that this approach would be faster than RegEx.
You seem to have a well-defined set of rules regarding what you consider to be valid input - you might consider using a hand-written LL parser for this. Such parsers are relatively easy to write and optimize. Usually you'd have the parser output a tree structure describing the input - I would use this tree as input to a matching routine that performs the work of matching the tree against the list of entries, using the rules you described above.
Here's an article on recursive descent parsers.
Assuming the rules are as you said: literal or start with a *.
Java:
public static boolean matches(String candidate, List<String> rules) {
for(String rule : rules) {
if (rule.startsWith("*")) {
rule = rule.substring(2);
}
if (candidate.endsWith(rule)) {
return true;
}
}
return false;
}
This scales to the number of rules you have.
EDIT:
Just to be clear here.
When I say "sort the rules", I really mean create a tree out of the rule characters.
Then you use the match string to try and walk the tree (i.e. if I have a string of xyz, I start with the x character, and see if it has a y branch, and then a z child).
For the "wildcards" I'd use the same concept, but populate it "backwards", and walk it with the back of the match candidate.
If you have a LOT (LOT LOT) of rules I would sort the rules.
For non wildcard matches, you iterate for each character to narrow the possible rules (i.e. if it starts with "w", then you work with the "w" rules, etc.)
If it IS a wildcard match, you do the exact same thing, but you work against a list of "backwards rules", and simply match form the end of the string against the end of the rule.
I'd try a combination of tries with longest-prefix matching (which is used in routing for IP networking). Directed Acyclic Word Graphs may be more appropriate than tries if space is a concern.
I'm going to suggest an alternative to the tree structure approach. Create a compressed index of your domain list using a Burrows-Wheeler transform. See http://www.ddj.com/architect/184405504?pgno=1 for a full explanation of the technique.
Have a look at RegExLib
Not sure what your ideas were for splitting and iterating, but it seems like it wouldn't be slow:
Split the domains up and reverse, like you said. Storage could essentially be a tree. Use a hashtable to store the TLDs. The key would be, for example, "com", and the values would be a hashtable of subdomains under that TLD, iterated ad nauseum.
Given your requirements, I think you're on-track in thinking about working from the end of the string (TLD) towards the hostname. You could use regular expressions, but since you're not really using any of the power of a regexp, I don't see why you'd want to incur their cost. If you reverse the strings, it becomes more apparent that you're really just looking for prefix-matching ('*.example.com' becomes: "is 'moc.elpmaxe' the beginning of my input string?), which certainly doesn't require something as heavy-handed as regexps.
What structure you use to store your list of entries depends a lot on how big the list is and how often it changes... for a huge stable list, a tree/trie may be the most performant; an often-changing list needs a structure that is easy to initialize/update, and so on. Without more information, I'd be reluctant to suggest any one structure.
I guess I am tempted to answer your question with another one: what are you doing that you believe your bottleneck is some string matching above and beyond simmple string-compare? surely something else is listed higher up on your performance profiling?
I would use the obvious string compare tests first that'll be right 90% of the time and if they fail then fallback to a regex
If it was just matching strings, then you should look at trie datastructures and algorithms. An earlier answer suggests that, if all your wildcards are a single wildcard at the beginning, there are some specific algorithms you can use. However, a requirement to handle general wildcards means that, for fast execution, you're going to need to generate a state machine.
That's what a regex library does for you: "precompiling" the regex == generating the state machine; this allows the actual match at runtime to be fast. You're unlikely to get significantly better performance than that without extraordinary optimization efforts.
If you want to roll your own, I can say that writing your own state machine generator specifically for multiple wildcards should be educational. In that case, you'll need to read up on the kind of algorithms they use in regex libraries...
Investigate the KMP (Knuth-Morris-Pratt) or BM (Boyer-Moore) algorithms. These allow you to search the string more quickly than linear time, at the cost of a little pre-processing. Dropping the leading asterisk is of course crucial, as others have noted.
One source of information for these is:
KMP: http://www-igm.univ-mlv.fr/~lecroq/string/node8.html
BM: http://www-igm.univ-mlv.fr/~lecroq/string/node14.html