I was using tesseract-ocr (pytesseract) for spanish and it achieves very high accuracy when you set the language to spanish and of course, the text is in spanish. If you do not set language to spanish this does not perform that good. So, I'm assuming that tesseract is using many postprocessing models for spellchecking and improving the performance, I was wondering if anybody knows some of those models (ie edit distance, noisy channel modeling) that tesseract is applying.
Thanks in advance!
Your assumption is wrong: If you do not specify language, tesseract uses English model as default for OCR. That is why you got wrong result for Spanish input text. There is no spellchecking post processing.
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I'm an Italian student approaching the NLP world.
First of all I'd like to thank you for the amazing work you've done with the paper " Higher-order Coreference Resolution with Coarse-to-fine Inference".
I am using the model provided by allennlp library and I have two questions for you.
in https://demo.allennlp.org/coreference-resolution it is written that the embedding used is SpanBERT. Is this a BERT embedding trained regardless of the coreference task? I mean, could I possibly use this embedding just as a pretrained model on the english language to embed sentences? (e.g. like https://huggingface.co/facebook/bart-base )
is it possible to modify the code in order to return, along with the coreference prediction, also the aforementioned embeddings of each sentence?
I really hope you can help me.
Meanwhile I thank you in advance for your great availability.
Sincerely,
Emanuele Gusso
SpanBERT is a version of BERT pre-trained to produce useful embeddings on text spans. SpanBERT itself has nothing to do with coreference resolution. The original paper is https://arxiv.org/abs/1907.10529, and the original source code is https://github.com/facebookresearch/SpanBERT, though you might have an easier time using the huggingface version at https://huggingface.co/SpanBERT.
It is definitely possible to get the embeddings as output, along with the coreference predictions. I recommend cloning https://github.com/allenai/allennlp-models, getting it to run in your environment, and then changing the code until it gives you the output you want.
I'm seeking advice on which version of Tesseract should I use to train for an ancient language that has unique letters. The language is very similar to Arabic in terms of characteristics. It also goes from right-to-left and some letter can connect in the word. In other words, a letter might have three shapes depending if it comes in the beginning, middle or end. It also has harakat (short vowel marks) that come above or below letters.
The reason I'm asking is because I want to take advantage of the tools available for version 3.X but this warning about Arabic threw me off since this language is very similar to it.
For anyone who's familiar with Tesseract, which version do you recommend to train for such a language? Also, if you are aware of a better tool, kindly share it please.
If you have a large amount of documents need to OCR, would recommend to use Tesseract 4.0 as it's faster in general. You may refer to below for more information in case you haven't read that before.
Tesseract 4.0 Accuracy and Performance
Tesseract 4.0 with LSTM
Training Tesseract 4.0
Language Data File for 4.0, you may have a test to see if the Arbic OCR works fine in OCR Engine Mode 1 (i.e --oem 1) which is Neural nets LSTM only.
Tesseract 4.0.0 alpha has been released since last Nov/Dec.
Hope this help.
I am working on an OCR related android app and I need to use multivariate logistic regressions for the classification of alphabets. My question is that that can I use Stanford classifier(http://nlp.stanford.edu/software/classifier.shtml) for character recognition? If it can train on a dataset of images? And if I can't then please suggest me a JAVA library for the purpose.
Great minds think alike. I was wondering the same thing. Specifically for OCR.
Even though it's almost a year after you asked your question.
It sounds simple enough; all you would need to do is normalize each character into a 5x7 array (or maybe 64x128), and then classify into the 26 upper and 26 lower case characters; plus 10 digits and 31 punctuation glyphs on a keyboard... Seems doable. Maybe when I get a round tuit...
It turns out that there is a Java library for OCR https://sourceforge.net/projects/javaocr/ and it's called Java OCR (surprise! :-) ). The only problem is that:
1. It doesn't work out of the box. It needs to be trained.
2. The documentation isn't very good.
3. People have had trouble getting it to work.
Good luck.
I just got a doubt it's not clear with the search engine results.
Can OCR (Optical character Recognition) read captcha, QR-code and barcodes?
Captcha.
QR-code.
Barcodes.
Licence codes
It depends on captcha. Standard OCR isn't meant for CAPTCHA breaking. Anyway simple captcha can be preprocessed and then fed to an OCR engine, sometimes it works... In general CAPTCHA breaking is much more complex than downloading the Tesseract binaries. If it were that easy, all of the paid services would be out of business overnight.
QR Codes and barcodes are both optical machine-readable data systems capable of conveying large amounts of data. Both are extremely useful in their own right. They have important differences but not regarding your question... so see point 3
The error correction capabilities of bar code recognition engines are way beyond that of OCR engines. A damaged bar code can easily be read. Also, most barcodes either work or they don't. OCR can confidently misread letters, while barcodes are "fail-safe".
I'm trying to use Tesseract-OCR to detect the text of images with pure text in it but these text has a handwritten font called Journal.
Example:
The result is not the best:
Maxima! size` W (35)
Is there any possibility to improve the result or rather to get the exact result?
I am surprised Tesseract is doing so well. With a little bit of training you should be able to train the lower case 'l' to be recognised correctly.
The main problem you have is the top of the large T character. The horizontal line extends across 2 (possibly 3) other character cells and this would cause a problem for any OCR engine when it tries to segment the characters for recognition. Training may be able to help in this case.
The next problem is the . and : which are very light/thin and are possibly being removed with image pre-processing before the OCR even starts.
Overall the only chance to improve the results with Tesseract would be to investigate training. Here are some links which may help.
Alternative to Tesseract OCR Training?
Tesseract OCR Library learning font
Tesseract confuses two numbers
Like Andrew Cash mentioned, it'll be very hard to perform OCR for that T letter because of its intersection with a number of next characters.
For results improvement you may want to try a more accurate SDK. Have a look at ABBYY Cloud OCR SDK, it's a cloud-based OCR SDK recently launched by ABBYY. It's in beta, so for now it's totally free to use. I work # ABBYY and can provide you additional info on our products if necessary. I've sent the image you've attached to our SDK and got this response:
Maximal size: lall (35)