I would like to learn my training file for tesseract new letters. I want use win 10 (I won't use linux) - for use tesseract Nuget-package in c#.net app.
I tried jTessBoxEditor but it's not working (first time error in registry, than cannot found fonts, than problem with java, than text2image doesn't work properly...). Editor SunnyPage could not even load the image without fail.
which program use for separating letters and creating training file as windows user
should I use tesseract or other OCR engine? It looks like tesseract isn't windows-user friendly
please post example training file for this three images - if there is any need of preprocessing (scale etc.) it should be done programaticaly (c#.net)
Which program use for separating letters and creating a training file?
Try this one: https://github.com/skotz/captcha-breaking-library
or:
OpenCV
OpenCV is a popular framework for computer vision and image processing. It is easy to use OpenCV to process the CAPTCHA images. It has a Python API so you can use it directly from Python.
Keras
Keras is a deep learning framework written in Python. It makes it easy to define, train and use deep neural networks with minimal coding.
TensorFlow
TensorFlow is Google’s library for machine learning. If you will be coding in Keras, but Keras doesn’t actually implement the neural network logic itself. Instead, it uses Google’s TensorFlow library behind the scenes to do the heavy lifting.
This involves either brute-forcing the captcha or running OCR algorithms on it to try and detect what is written in the captcha.
If you want to implement your own CAPTHA an algorithm please look into that abstract: http://cmp.felk.cvut.cz/~cernyad2/TextCaptchaPdf/DESIGNING%20CAPTCHA%20ALGORITHM%20SPLITTING%20AND%20ROTATING.pdf
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.800.3065&rep=rep1&type=pdf
Related
I am building a code and trying to keep things as generic as possible. I have seen a number of tutorials and post but they are all platform specific (tensorflow\pytorch).
Is there a good way to load and use a previously trained neural network model in a manner that the code will be able to cope with both torch and tensorflow? Does it matter in which version of tensorflow\torch the network was built in? I want the code to be as generic as possible.
Also, do I need to know the structure of the original network or can I load it and use it without the notion of the structure?
I don't think it is possible to write a program that can load pre-trained models from both Torch and Tensorflow as they save in different formats.
You might want to look into the Open Neural Network Exchange Format (https://onnx.ai/) if you are creating the models yourself, this is an initiative backed by Amazon, Facebook, Microsoft, and others to create a portable file format for deep learning models.
I am trying to record and calculate its spectrum in real time. I wonder what library I should use for this purpose. I need to integrate it into my original C++ code and combine with my image feature. I looked into OpenSmile and it seems not suit for this purpose.
Any suggestions are appreciated!!
Here are the pretty good comparison and evaluation of various audio feature extraction toolboxes.
Main conclusion based on the paper mention in the link below:
1) Essentia: Full function workflow environment for high and low-level features, facilitating audio input, preprocessing and statistical
analysis of output. Written in C++, with Python binding and
export data in YAML or JSON format.
2) Marsyas: Full real-time audio processing standalone framework
for data flow audio processing with GUI and CLI. This program
includes a low-level feature extraction tool built in C++, with an ability
to perform machine learning and synthesis within the framework.The
feature extraction aspects have also been translated to
Vamp plugin format
3)YAAFE Low level feature extraction library designed for computational
efficiency and batch processing by utilizing data flow
graphs, written in C++ with a CLI and bindings for Python and
Matlab
You can find the more details about Graph of Percentage Coverage of Multiple Feature Sets and Graph of Computational Time of Feature Extraction
Tools in the
link.
I’m looking a simple tool to train and test neural networks for classification tasks. It need not be very sophisticated tool and I just want to train and test simple data sets such as given in the following web site.
http://www.stats.ox.ac.uk/pub/PRNN/
It's not a pre-made utility, but you could roll your own quite quickly using the Encog neural network framework (for both Java and .NET).
I've used it before and it was quite easy to get to grips with. The documentation is quite good, and if you need it, I've also found support on the forums to be good.
* UPDATE *
I just remembered that Encog does actually ship with a pre-made utility called Encog Workbench, which should do what you want.
I saw that the tool weka is having a gui interface. This gui interface is very easy for non coding users to classify data sets into classes. Matlab is very difficult since say for example making a neural network you need to write code and to do that you need to have a solid understanding of whats going on. Are there other tools like weka or else is there a plugin to matlab that gives more power to it?
RapidMiner has a functional GUI, and will work for both classification and clustering. It is the most popular open-source (free) data mining application available as of 2012.
RapidMiner: http://rapid-i.com/
It also has numerous training videos and tutorials that you can follow along with - I learned basic clustering methods using a K-means cluster method in about 3 hours. See the Vancouver Data blog for some great RapidMiner analytics videos. Top-notch stuff, really.
Vancouver Data (Neil McGuigan): http://vancouverdata.blogspot.com/
As a bonus, you can install the Weka plug-in, which then gives you GUI Weka. All of the add-ons are free and well-integrated. Other add-ons include a GUI 'R' (the stats program), Reporting Services, Text and Web Analytics, etc. It is fairly simple to use straight 'out of the box' (IMO).
Weka is very (very) powerful and you can write your own classifier if that's what you need to do.
Between Matlab and Weka there's pretty much nothing you can't do in terms of Machine Learning.
You might want to check out Netlab toolkit for Matlab, which is a neural network toolkit developed by a Professor at Aston University - it is available from http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/
Hi I trained my network into Matlab neural network toolbox I save it.. I can used into matlab environment by calling sim(net,p) but How can I export it to anther .net apps ?
You might be better off exporting just the weights (etc.) element of the network and using them in a library designed for .NET like AForge.NET, rather than deal with all of the interop.