i'm wondering what is the best method to train Tesseract (kind of text/TIFF and so on) for a particular kind of documents, with these particularities:
the structure and main text of the documents is always the same
the only things that change are 5 alphanumeric codes (THIS ARE THE REAL IMPORTANT THING TO DETECT!)
Some of thes codes are bold
At the moment I used standard trained datas, I detect the entire text and I extrapolate the codes with some regular expressions.
It's okay, but I've got errors sometimes, for example:
0 / O
L / I / 1
Please someone knowns some "tricks" to improve precision?
Thanks!
during training part of Tesseract, you have to make a file manually to give to the engine in order to specify ambiguous characters.
For more information look at the "unicharambigs" part of the Tesseract documentation.
Best Regards.
Related
I have e extracted the elliptic Fourier descriptors for each otolith; but couldn't figure out how to normalize them with respect to the first harmonic and how to reconstruct mean shapes from them for each stations. I try myself, but couldn't get any results using Momocs pacage. Need expert helps in R script. Data in excel file
to use "first harmonic" normalization, just pass efourier() with default parameters (ie with norm=TRUE).
Have a look to Details section in ?efourier since this is usually not the best way to go (and I think it's very valid for otoliths)
feel free to contact me directly !
all the best
I want to predict the occurrences of the word "repeat" in a speech as well as the word's approximate duration. For this task, I'm planning to build a Deep Learning model. I've around 50 positive as well as 50 negative utterances (I couldn't collect more).
Initially I've searched for any pretrained models for keyword spotting, but I couldn't get a good one.
Then I tried Speech Recognition models (Deep Speech), but it couldn't predict the exact repeat words as my data follows Indian accent. Also, I've thought that going for ASR models for this task would be a over-killing one.
Now, I've split the entire audio into chunk of 1 secs with 50% overlapping and tried a binary audio classification in each chunk that is whether the chunk has the word "repeat" or not. For building the classification model, I calculated the MFCC features and build a sequence model on the top of it. Nothing seems to work for me.
If anyone already worked with this kind of task, please provide me with a correct method/resources to build a DL model for this task. Thanks in advance!
I am new to nltk, and I find wordnet functionality pretty useful. It gives synsets, hypernyms, similarity, etc. But however it fails to give similarity between locations like 'Delhi'-'Hyderabad' obviously as these words are not in the wordnet corpus.
So, I would like to know, if somehow I can update the wordnet corpus OR create wordnet over a different corpus e.g. Set of pages extracted from wikipedia related to travel? If at all we can create wordnet over different corpus, then what would be the format, steps to do the same, any limitations?
Please can you point me to links that describe the above concerns. I have searched the internet, googled, read portions of nltk book, but I don't have a single hint to above question.
Pardon me, if the question sounds completely ridiculous.
For flexibility in measuring the semantic similarity of very specific terms like Dehli or Hyderabad, what you want is not something hand-crafted like WordNet, but an automatically-learned similarity measure from a very large database. These are statistical similarity approaches. Of course, you want to avoid having to train such a model on data yourself...
Thus one thing that may be useful is the Google Distance (wikipedia, original paper). It seems fairly simple to implement such a measure in a language like R (code), and the original paper reports 87% agreement with WordNet.
The similarity measures in Wordnet work as expected because Wordnet measures semantic similarity. In that sense, both are cities, so they are very similar. What you are looking for is probably called geographic similarity.
delhi = wn.synsets('Delhi', 'n')[0]
print delhi.definition()
# a city in north central India
hyderabad = wn.synsets('Hyderabad', 'n')[0]
print hyderabad.definition()
# a city in southern Pakistan on the Indus River
delhi.wup_similarity(hyderabad)
# 0.9
melon = wn.synsets('melon', 'n')[0]
delhi.wup_similarity(melon)
# 0.3
There is a Wordnet extension, called Geowordnet. I kind of had the same problem as you at one point and tried to unify Wordnet with some of its extensions: wnext. Hope that helps.
I am dealing with an hard assignment which I could not move the pen. What is the way to solve the following problem? Any help would be appreciated.
f(x)=1/x and x is between 0.1 and 1
The problem is asking to traing the network by using back propagation algorithm with one hidden layer.
Trainin set will have 200 input/output pattern, test set will have 100 and validation will have 50 patterns.
How can I solve this? Regards.
That sound much more complicated than it actually is. The network does not know anything about what you actually want to represent with the input and output pattern. So do not worry about that. All you need to do is setup such a network (I assume that you know how to do that - otherwise just check around there are couple of libs, but it is even possible in Excel to set it up quickly for testing purposes)
Then just run the test data against the network in a loop. Once the network is kind of stable store it and start testing.
I assume the representation of the patters has been defined already? It's one of the most important point that defines the quality. The closer the x/y pairs are semantically the closer the representation patterns have to be - meaning here the delta between x/y pairs. In particular for the small x value/large y pairs!
Otherwise the network will not "understand" that and you can teach forever - since there is no correct representation of the similarity - in this case the delta x and delta y
For example the value 7 in binary format is not close at all to the value 8. Meaning if the network did not "learn" that because it has never seen the 8 it will not work well.
So the closer the values the more similarities the representation of the values should be for the network! - That's the key.
Tweaking the parameters will then fine tune your model
I need to analyse a binary data file containing raw data from a scientific instrument. A quick look in a hex viewer indicates that's probably no encryption or anything fancy: integers will probably be written as integers (but I don't know what byte order), and who knows about floating point.
I have access to a (closed source) program that can view the contents of the file. So I can see that a certain value is 74078. Actually searching for that value I'm not sure about - do I search for 00 01 21 5E, some other byte order, etc? (Hex Fiend doesn't support searching for decimal values) And how would I find a floating point number?
The software that produces these files runs on XP. I'd prefer tools that run on OSX if possible.
(Hmm, I wrote up this question, forgot to post it, then solved the problem. I guess I will write my own answer.)
In the end, Hex Fiend turned out to be just enough. What I was expecting to do:
Convert a known value into hex
Search for it
What I actually did:
Pick a random chunk of hex that looked like it might be a useful value
Tell Hex Fiend to display it as integer, or as float, in either little endian or big endian, until it gave a plausible looking result (ie, 45.000 is a lot more plausible than some huge integer)
Search for that result in the results I had from the closed source program.
Document it, go back to step 1. (Except that normally the next chunk wouldn't be 'random', but would follow sequentially.)
In this case there were really only three (binary) variables for how to interpret data:
float or integer
2 bytes or 4 bytes
little or big endian
With more variables the task would be a lot harder. It would have been nice if Hex Fiend could search for integers/floats directly, perhaps trying out the different combinations. Perhaps other hex viewers do.
And to answer one of my original questions, 74078 turned out to be stored as 5E2101. A bit more trial and error and I would have got there. :)
UPDATE
If I was doing this over, I'd use "Synalyze It!", a tool designed for exactly this purpose.