I have some questions about making tiff/box files for tesseract 4.
In TrainingTesseract 4.00 document written:
Making Box Files As with base Tesseract, there is a choice between
rendering synthetic training data from fonts, or labeling some
pre-existing images (like ancient manuscripts for example).
But it did not explain how to train with pre-existing images.
I want to train for the Persian language in tesseract 4 (lstm). I have some images from ancient manuscripts and want to train with images and texts instead of font. So I can’t use text2image command. I know that the old format box files will not work for LSTM training.
How can I make tif/box for tessearct 4 lstm then label them and
how to change tesseract commands?
Should I use other tools for generating box files (Given that Persian
language is right to left )?
Should I use fine tuning or train from Scratch?
I was struggling just like you, until I found this github repository:
https://github.com/OCR-D/ocrd-train
It will make your life super easy. All you need to do is to put your images in tif format and your text should have the same image name with extension .gt.txt. It will take care of all the rest for you. (you might need to update the Makefile according to your local machine)
Whether to train from scratch or fine-tune depends on your own language, data and the problem you are trying to solve. For me the fine tunining is what I need cause I am happy with the current performance but need to add upon it.
All the useful details you might need can be found in this answer
1) Use below command to make lstmbox:
tesseract test.tif test-lstmbox -l eng --psm 6 lstmbox
It will make a lstmbox for you but you have to correct the character in box file.
2) You require enough data for training from Scratch So I suggest fine tuning is better option.
Related
I am trying to use NVIDIA DIGITS to perform image segmentation (by following this article). After training the model, I was able to perform the segmentation on individual images.
Now I would like to give a list of test images and labeled images to DIGITS, and I want it to calculate accuracy for the input images. Is it possible to do it using DIGITS, if yes, then what are the steps? (options to select on framework while training/testing model, format of input file, folder structure, etc.)
Also, if DIGITS can't do it, then are there any other frameworks? (I read this post, but it would be better if I can directly use some plug-and-play framework, to begin with)
Thanks!
I am using tesseract to get text from an image, I am only interested in numbers. I have trained tesseract and created a new language that is the exact font in the image and the training data only included numbers. In the training data I also included every possible value that would be in an image, 1-5000 to be specific and also created a wordlist of these same values. However it still mixes up 1 and 7, as well as sometimes 3 and 8. Does anybody have any recommendations on whether I should retrain differently or do some processing on the image before giving it to tesseract?
Make sure there are at least 20 instances of every character in the training texts you provide to tesseract. I give at least 6 pages of the same font to have a decent training sample size.
2.Tesseract Text Recognition also depends on the image quality. Check out possible preprocessing algorithms you can use: Improve Quality of Tesseract
Take a look at the number_dawg file. Modifying it can help recognising digits.
I am using Caffe to extract features with matlab wrapper.I have 5011 images as test data set.I chopped all the layers after 'relu7' in 'deploy.prototxt'. I found out if you take the same image as input of matcaffe_demo.m and matcaffe_batch.m, you will get the different 4096-dim features.
Could someone tell me why?
what is the differences between you extract features from all these images one by one with matcaffe_demo.m and extract features by listing all these images with matcaffe_batch.m?
You can find the answer to this question at caffe github.
Basically, matcaffe_demo is used for classification and it averages results of 10 crops of the input image, while matcaffe_bathc uses only a single input.
Moreover, note that these m-files are no longer available in recent caffe versions.
This question was posted on Matlab Central (http://goo.gl/MkU8P) but hasn't gotten any answer. I thought someone here might have a lead.
I use publish() quite a bit to produce online class notes. It's working well but frankly, I find the PNG plots to be of awful quality. I've tried setting figureSnapMethod='antialiased' and that's a bit better but I mostly find the result fuzzy.
What would be awesome is to produce SVG plots. Is there any way to do that? I suppose it would have to involve plot2svg somehow (also from File Exchange) since SVG output seems to be only supported for Simulink models. Or perhaps someone has a better solution using some other high quality format.
before using publish you should set some options to change imageForamt. by default it is set to PNG format which is not good for reports, papers, etc. what you need is a vector graphic image, something like an eps or emf file formats. (in such case it does not matter how much you zoom in , it never becomes fuzzy or low resolution).
Any image format that your installed version of Microsoft Office can import, including 'png' , 'jpg', 'bmp',and 'tiff'. If the 'figureSnapMethod' is 'print',then you can also specify 'eps', 'epsc', 'eps2', 'ill', 'meta',and 'pdf'
for HTML output any format publishes successfully
for latex output any format publishes successfully
I suggest you to use eps, it is the best. and remember SVG is not supported in publish. ;)
you should add this part to your command:
'imageFormat','eps'
I saw different articles speaking about OCR form recognition (data extraction) and they said that they used Neural Network in order to do form recognition, so what's the relation between Artificial Neural network (ANN) and form recognition? If I want to extract fields from a BusinessCard, is it required to use ANN or is it optional? In other words when do I need to use ANN and when I don't?
It's a little different. ANN is just an "expert" in all OCR. But OCR engines contain many experts. When you study ANN you will build a simple OCR engine using just ANN but this does not compare to modern engines that use this in conjunction with tri-grams, morphology, data types ( very important for BCR and Forms ), dictionaries, connected components algorithm, etc. So look at it as just one of the tools in the bag of tricks to extract quality results. A good engine will incorporate ANN and all the others. In BCR there are additional considerations and it should be very heavy on connected components, dictionaries first, then use ANN and pattern matching for the actually recognition.
ANN is one way to perform OCR. There are others. Hence if you want to extract fields from a BusinessCard using ANN is only optional.
Good question. I recently spent some time playing with OCRopus, a Google project that does OCR - you can get it for free and play with it yourself. I'm pretty sure that it has an ANN as one of the modules behind it. However, the whole process of Optical Character Recognition can have many steps (lots of different little modules that each do something and pass the results to the next module).
So, here are some of the things I remember as being done by modules in that project:
There was a module that turned the image into black and white - this makes it easier for later modules to deal with.
Getting rid of speckles / spackles.
Straightening out the lines of text.
Breaking lines of text into individual words (it's been a few weeks, not sure about this one)
Basically, you can do the above using little bits of code that don't involve a neural net. So it's simpler doing it with these little bits of code.
The neural net I think is used just to recognize the individual characters - which character of a group of possible characters is it.
There's a training command in the OCRopus that I had running for over a week on end, and it kept sending line samples to the map, slowly changing the map as it went. I think it was training the ANN part.