What is Reddit's opinion of Tesseract?

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What you're looking for is Optical Character Recognition (OCR).

You can actually do this with Google Drive or Tesseract really easily and effectively.

There are also plenty of other OCR packages out there too.

> which is a fairly complicated technique involving pattern matching.

The technique is complicated, but We Have the Technology. The future is now.

https://github.com/tesseract-ocr/tesseract#running-tesseract

tesseract imagename outputbase [-l lang] [--oem ocrenginemode] [--psm pagesegmode] [configfiles...]

This neural net OCR engine, open source and maintained by Google, makes it one command.

(By the way, somewhat relevant, Google indexes images using OCR now. Searching my Steam name yielded an untitled, uncaptioned, and untagged screenshot of the friends list of one of my friends.)

Use tesseract 4.0, which now uses an LSTM. You can train it on your own data too.

They also have some docs describing the system and the typical OCR pipeline

I've been able to cobble together something approximating what you describe using "bleeding edge" version of tesseract ocr (it suddenly got.... GOOD....)

https://github.com/tesseract-ocr/tesseract/wiki/4.0-with-LSTM

I use it, in combination with a bunch of bash scripts to do things like deskew the images &c, to take as input a "scanned piece of paper PDF" and have as output the same thing except with a text layer.

I suggest you check it out?

<shameless self promotion -- also, I've been wondering if there's a possible market for this sort of service....>

What you're talking about is Optical Character Recognition. A bit of web searching turned up pytesseract, a Python wrapper for the Tesseract library. I haven't done any OCR so I can't comment on what the error rate might be, or if there are better alternative libraries.

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I'd suggest that if you can successfully do OCR, you first write the resulting data to a csv file and import that into your spreadsheet application of choice. Then if you want to do further automation, look into creating a spreadsheet file directly.

Tesseract and ~~probably~~ most of the prequel scripts with time stamps. ~~Someone else said a list of quotes, but it guessed a weird quote from a random SW film earlier today so I think has all of the scripts.~~

EDIT: Added links. Rogue one looks like the whole film, but the others look like maybe just most of them.

I'm a not an OCR expert either, but I've used tesseract a few times and it's quite impressive. Of course the ocr will not be 100&#37; perfect but if your input is a good quality picture, and the handwriting is ~ok, you should have something to work with.

You are looking for OCR, or Optical Character Recognition. There are a few libraries for this in Python. Most if not all of them are based on Tesseract. Here is a Python lib.

Isto demonstra simplesmente que o captcha (que está lá para prevenir acesso de bots) pode ser derrotado por um OCR (neste caso o tesseract ) sem que tenhas de "preparar" a imagem de qualquer forma. Uma boa solução de captcha distorce os caracteres o suficiente para que não seja fácil um bot o conseguir resolver.

É facílimo criar uma lista de todas as matriculas portuguesas - são apenas combinações de 6 caracteres em 34(?) caracteres possíveis.

É facílimo criar um script/bot que itere todas estas matriculas e preencha o formulário do site (captcha inclúido) e peça e armazene todos os dados.

Isto não me diz respeito já que não tenho carro. Mas a informação (qualquer que ela seja) pode ser toda facilmente extraída do site nuns dias, basta haver alguém motivado para isso.

Don't worry, your questions were fair and it's not something we could just look away from either.

About the screenshot automation: It's split in two major parts. Taking the screenshot and converting it with Tesseract. And afterwards the resulting string is interpreted by a bunch of logical conditions that provide every item in a message as its own request. Statistcs are calculated on the server-side.

If you want an SDK for this you can use https://github.com/PaddlePaddle/PaddleOCR or https://github.com/tesseract-ocr/tesseract

You can do this from a desktop app like https://github.com/RajSolai/TextSnatcher

The Tesseract OCR engine (from Google, opensource) is very powerful. It does not have its own GUI though. So you can try a few third party frontends to Tesseract from this list:

https://github.com/tesseract-ocr/tesseract/wiki/User-Projects-%E2%80%93-3rdParty

For instance tesseract4java has batch recognition in its short description there

I don't know of any web framework that integrates OCR by default, haskell or otherwise. However, as a zeroth-order approach, you could integrate tesseract into your app, i.e., run the command-line tool against your image.

paperless is based on tesseract. it's an OCR library and it's very good. I made a lot of tests with it and image to text (and PDF) is very reliable.

Regarding paperless itself I tried it but I don't like the fact that it moves documents around in the filesystem. If that doesn't bother you I guess it's okay.

Google's cloud vision is definitely better if you compare it with tesseract out of the box. I tried comparing them for scene text detection and Google cloud was the clear winner. On the other hand, I got tesseract to work better after making some modifications. Tesseract has a ton of options, and can work really well if you take time to understand and re-train it even. See their awesome wiki here : https://github.com/tesseract-ocr/tesseract/wiki

I guess that you're looking for OCR tools such as tesseract.

If you want to train your own OCR it's a much more difficult task. You have to train a ML algorithm able to detect words on the image, then create a corpus where each of this word is correctly labelled (#captcha) and to finish I'd use a CNN+LSTM architecture.

In case you were looking for actual answers rather than more memes: from the GitHub page the bot links to, it looks like it uses a combination of OpenCV and Tesseract OCR to detect the image in the text. OpenCV is an open source computer vision library that’s been in development for a long time. As for Tesseract OCR, I don’t know much about the specifics of that library but OCR stands for optical character recognition, a common algorithm/technique to transform an image of text into the machine encoded text.

Tesseract. Free in either sense, supports about a hundred languages out of the box. UB Mannheim has a pre-built binary package for Windows you can download here.

I have messed with Apple's face detection API in the Core Image framework and it worked perfectly for me. Apple's face detection framework is very powerful and advanced, haven't fully tried out all the things you can do with it. When I was messing around with it I got it working so it could detect a person's emotion from the picture (happy, sad, etc). In your case assuming you want to compare two images and see if they match, I can see that being possible with using Apple's SDK (assuming you already have a database of the profile pictures that you can compare too). I would personally look more into the documentation for Face Detection.

If you're looking to grab text from an image I would highly recommend checking out Tesseract OCR. I've personally used Tesseract before in the past and had no problems with grabbing an image with text from a user and then out printing it in plain text. It does have some limitations but none that would likely affect you. It's also very easy to integrate into your app.

Hopefully, this answered some of your questions.

Links: Documentation for Face Detection https://developer.apple.com/library/content/documentation/GraphicsImaging/Conceptual/CoreImaging/ci_detect_faces/ci_detect_faces.html

Tesseract. https://github.com/tesseract-ocr/tesseract

What I used in the past for one of my projects. https://github.com/gali8/Tesseract-OCR-iOS

You could take a look at Abbyy.com I think they sell a product that can do that. Note that it is targeted for corporate/enterprise market and priced accordingly.
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For an "on the cheap" solution I'd probably try something like this:

• Scan document to TIFF (just because a 'flat' image file is easier to futz with)
• Use imagemagick "-crop" option 3 times.
  
  1. grab zone x to file x.tif.
     
  2. grab zone y to file y.tif.
     
  3. grab zone z to file z.tif.
     
• Use tesseract to turn x.tif to x.txt (repeat for y and z).
  
• Use batch (or VBS or bash or powershell) to extract the text from 3 .txt files.
• Rename/move the tiff file according to desire using results of script/batch.
  
• Optionally convert tiff to pdf
  
• Delete the temporary crap files.
  

EDIT: links and formatting
EDIT2: and if that info doesn't get you going you probably should shell out money

I can not stress the following enough! Flask is a web framework. It is not supposed to be the core component of your system unless you're building a straight forward CRUD application which you're clearly not. I'm saying this because you're asking in /r/flask which is a niche subreddit. /r/Python or even more generic subreddits like maybe /r/programming would have been better choices because most importantly you should use the right tool for the job. And while Flask is IMO a very good choice for a web frontend Python is probably not the best tool for the document processing. For example, OCR comes to mind where your best choice is probably tesseract.

> What are some hosting complications that can be expected when file uploads are involved? Files will have a hard limit on size and will be renamed using a UUID, is there anything else I need to worry about (DDOS or security wise)?

Do some "sharding". Having too many files in one directory will decrease performance pretty soon.

> Are there best practices for managing user-submitted files semi-securely (There's a login required and will be an automatic deletion after the analysis of the document is complete)?

https://speakerdeck.com/danjou/protecting-static-files-in-your-web-app

> What are some good hosts for managing a service like this and what kind of budget should I expect this to cost? I think I can get away with GAE or Heroku, but maybe there's something I don't know about that could work on a hobby budget?

For this kind of project you'll want full root shell access on the server. I don't know if Heroku or GAE provide that, I doubt it. Digital Ocean or Linode and dozens of other providers offer VPSes starting at 5$/month which should be in budget I guess.

It looks like Python-Tesseract just calls the CLI for the locally installed Google Tesseract OCR Engine, so it shouldn't require any external requests.

Tesseract OCR is a solid OCR tool. Looking at how it's built may inform your approach, and you may even be able to feed Tesseract's network outputs into your own model to improve it.

Awesome project idea! A couple thoughts I had looking at your ideas:

I'd look into using an Optical Character Recognition (OCR) library to do your text detection instead of writing your own in OpenCV. Something like Tessaract looks like it would be a pretty good solution. Even better- it has a Java API, so that might be a reason to learn Java :) You might encounter some issues getting it to work on Android; I've only tried it on Windows/Linux myself.

As for a choice of languages, If you're really interested in mobile development, I'd strongly recommend learning Kotlin and/or Java. At least for android, those are the two major languages being used and they will probably be the most useful.

And since you're writing this for yourself, don't worry about all the stuff about encryption and compliance :) Those only become relevant when you are holding people's financial data or using their credit card info as a method of payment.

The task you're trying to perform is commonly known as "Optical Character Recognition", or OCR.

Try starting with a basic set of image processing techniques such as:

• OpenCV for rotating images
• Tesseract for OCR

Here's a full tutorial that looks quite good.

Your life will be made easier by the fact that the serial codes have fixed form. You should therefore be able to improve the performance of whatever model you end up with by writing a regex and filtering solutions on that. One brute force way to handle the tilt of the images would be something like:

1. Rotate 5 degrees clockwise, run through model
2. Does output satisfy regex? If so, return that output. If not, back to step 1.

IMO, pdfsandwich is one of the greatest utilities, ever. (Don't do research without it!)

It does OCR using unpaper and tesseract, so that's probably a good place to start.

There's a few methods to get tesseract to detect information on a given image.

You can use blurs and similar techniques. There's plenty of tutorials online discussing how to extract information using OCR.

Tutorial using blur and thresholds: https://www.freecodecamp.org/news/getting-started-with-tesseract-part-ii-f7f9a0899b3f/

OCR by Google: https://github.com/tesseract-ocr/tesseract You can also get a forks of tesseract for js.

Hope this help :)

I'm assuming you're talking about the OCR component, and if so it was rather straight forward. I just used Tesseract OCR and its Python wrapper pytesseract to do some very minimal processing. I've had to deal with OCR for work reasons before and in my experience Tesseract it is probably the best open source one you can get. The examples on the pytesseract page were really all I needed to figure out how to use it, but reading the actual Tesseract docs helped me fine tune the specifics.

You could use something like tesseract to generate accompanying txt files with the same basename. Not ideal but you would be able to search for the picture in two steps.

I think what you are looking for is optical character recognition.. you can use something like pyautogui to take screen shots https://github.com/asweigart/pyautogui

and use https://github.com/tesseract-ocr/tesseract to recognize characters from images.

There are two steps. Detecting the text and its location, then recognizing what the text itself says. OpenCV is probably the library that you'd use to build the first part , and Tesseract is probably the one you'd use for the second part.

That error looks pretty clear: tesseract is not installed. Download it here: https://digi.bib.uni-mannheim.de/tesseract/tesseract-ocr-setup-3.05.01.exe

Full tesseract documentation: https://github.com/tesseract-ocr/tesseract/wiki

It uses Tesseract: https://github.com/tesseract-ocr/tesseract

The version 4 has neural network learning and training options, which I'm currently looking into and trying to make sense of. When I get to the point that the results cannot be improved anymore by tunning the image I will start with that.

It uses Tesseract: https://github.com/tesseract-ocr/tesseract

The version 4 has neural network learning and training options, which I'm currently looking into and trying to make sense of. When I get to the point that the results cannot be improved anymore by tunning the image I will start with that.

> So, before start throwing data/images to Tesseract, I should get rid of or bluring any background colors and probably images at the background, if they are any.

No. You should convert it into an image where each pixel is either completely black or completely white. Tesseract can handle some noise, but sometimes it's necessary to manually remove some more.

The wiki has some tips

Ah, my understanding of the problem was that you wanted them combined into one document, my solution doesn't work if you want them combined as text.

However, tesseract can read images and convert them to text... you would probably need to reformat them to get them back to a PDF, I have a few success stories of using LaTeX and OCR to recreate books as PDFs.

~~AFAIK Tesseract does not provide segmentation, or did this change recently?~~

I am wrong, it does provide some kind of segmentation, and the segmentation level is adjustable:

https://github.com/tesseract-ocr/tesseract/wiki/ImproveQuality#page-segmentation-method

Here's my dystopian future that involves this - AI gets better, but not to the point where humans are completely useless. All employment involves training an AI and ensuring that the data it gets fed actually trains it properly. A good example of this is training Tesseract to recognize a new font.

That's actually a really hard problem, as shown by the amount of development and engineering that goes into, say, training DeepMind to play Go.

If you don't have those skills, you are irrelevant. To give an example, I do electron microscopy at Intel. My future boss would say, "Alright, we need you to train the computer to do InfernalLake jobs. It's doing SoulWell chips just fine, but it's falling apart on the new process." The fact that I can do InfernalLake jobs is completely irrelevant because I'm only one person. They're going to take the AI that I train and deploy it to fifty microscopes, which will run 24 hours a day. So, the fact that I'm a good tech means nothing. The only value I bring is utilizing my technician knowledge to make the AI better.

Extracting skill values would actually be as simple as measuring the width of the skill bars and comparing to the maximum of 20. As for text (age, traits etc.), there's a great open source OCR app called Tesseract which can be called from the command line. At least that's the approach I took when cheesing myself some "perfect" colonists. Also, OP's post explains why decent cooks are so damn hard to come by.

Hello,

I worked on such a project last weekend. I used python for language, tesseract for the OCR lib and pytesseract for it's python wrapper, and finally the lib pogoiv for the IV computation.

For prototyping, I used NOX to automate PC screenshots (which is very fast). But you could easily use actual screenshots file, or even plug your android phone via USB and use ADB to remotely take screenshots from the python script. It's very slow, but it does work.

However, there are some issues: the dynamic background behind the CP values (like the bubbles for water type pokémons) they randomly makes the OCR fail. Also sometimes the pokémon 3d model gets in the way and blocks either CP value or Name.

If someone asks, I'll publish the code, but it's really simple.

resources:

https://github.com/tesseract-ocr/tesseract/wiki https://pypi.python.org/pypi/pytesseract/0.1 https://github.com/tmwilder/pogoiv

Oh and about the OCR I had good success with tesseract, which was originally made by HP in the eighties and nineties, open sourced in 2005 and mainly developed by Google since 2006. I believe it's what they use for their Google Books Library Project internally.

You can even give training data to tesseract in case you encounter a strange font etc. I used the command line because I had like 120 similar files anyway but for just the odd scan you should probably have a look at front ends or GUIs based on it, they list some on their Github page here

In case the new scanner you're getting doesn't provide good-enough OCR.

The best OCR system is Tesseract. It's free and very accurate. However, it's not exactly easy to use, you'll probably need someone computer-savvy to set it up for you.

also

Use something open source https://github.com/tesseract-ocr/tesseract

http://vbridge.co.uk/2012/11/05/how-we-tuned-tesseract-to-perform-as-well-as-a-commercial-ocr-package/

Or store a hash of each magnet, or use some kind of symbol that identifies each magnet.

We really need more varied project names. The domains are entirely different but the collision can only sow confusion.

The project itself does look fantastic though.

Tesseract is the best open source option, you can find it here

And to find a good example of how to use it check out this project.

Oh, that might work, i'll take a look thank you. I was playing around with some ideas[Googling stuff] last night and came across https://github.com/tesseract-ocr/tesseract which looks like it might be promising if i can keep the image fairly well controlled.

I was hoping for a straight forward Pi install but i think i will need to install home-assitant in a docker on the Pi so i can have a part of the machine dedicated for processing stuff like this.

Under the hood it uses OCRmyPDF which in its turn uses tesseract which means almost any language can be detected (they advertise it with - "supports more then 100 languages"). Yes, it supports Greek language as well.

Take the slides and use OCR software to extract the text. Then there are various Web sites where a block of text can be pasted in and standard reading scores (e.g. Flesch-Kincaid) calculated.

(Do not run spelling/grammar check … it will run riot on the extracted text. Retyping the text from slides is not recommended as it breaks the brain).

I never bothered with it because my handwriting sucks but I made suggestion on how in this subreddit multiple times.

First, as others are being pedantic so can I. Unless you write like a typewriter you probably want HWR (Hand written recognition) rather than OCR (Optical character recognition). I share few example on doing OCR either remotely, e.g desktop or self-hosted server, or locally, direction on the reMarkable. It works but the result, again especially on my hand writing, are basically rarely usable.

For OCR I used Tesseract https://github.com/tesseract-ocr/tesseract which is available via toltec on device. If you want to look at more modern approaches https://huggingface.co/microsoft/trocr-base-handwritten might be interesting.

Anyway assuming you've done that for every document, using e.g rsync on .rm files (should take few seconds) you can convert to e.g SVG strokes then (and that's the wasteful step) to e.g PNG/JPG to feed it to OCR. Once that's done you can index the content for each note or just straight up grep it.

Je ne pense pas. L'’outil tesseract (d’abord développé par HP dans les années 1985-1995 puis abandonné. Résurrection en 2005, quand le code est libéré (sous licence Apache), et le développement reprend sous la houlette de Google. Le code est libre et ça n'utilise pas de moteur google. Lien Wikipedia. Lien Github

Tesseract has Rust bindings and has been around for a while. It's not clear from your description what you're trying to accomplish, but I think for OCR, tesseract is an easy OSS way to start.

there are a few ways you could automate this data entry process, depending on the format of your order forms.

if your order forms are in pdf format, you could use a tool like tabula (https://tabula.technology/) to extract the data from the pdf and then import it into google sheets.

if your order forms are photos or faxes, you could use an ocr tool like tesseract (https://github.com/tesseract-ocr/tesseract) to convert the images to text so that you can then parse the text and import it into google sheets.

If you are on a Mac and know how to use the command line and how to install packages with Homebrew, give this a try:

https://gist.github.com/gordyt/9e59b2ade9cbc1271bbf6218303c2fd2

Save it somehere in your $PATH and set the execute bit (Example: chmod a+x pdf-ocr-txt)

If you run the command with no inputs you will get this:

usage: /Users/gordy/bin/pdf-ocr-txt file1 ...

Extract text from each (pdf) file and save it to a file with the same
name as the source file but with a .txt extension

Example:
- original file: sample.pdf
- new file: sample.txt

It uses pdf2image to convert each page of your PDF to an image.

Then it uses tesseract to analyze each image, extracting the text.

Then saves the result into a text file.

See the note at the top of the file for what you need to install with homebrew.

You can use a usb-c to c cable and the web interface to download your RM2 docs as PDFs.

I literally just threw this together so I'm sure it can be improved. But at least will give you something to start with.

I didn’t mention the name of the spambot just in case they have some kind of keyword monitor that they’re using to monitor and circumvent potential spam filtering tools.

That being said, the tool is using a tesseract-related wrapper, and it might be having trouble picking up the text.

Also, another thing I noticed about the code is that it only activates if it detects 4 or more words in the image. Did the image you post include 4 or more words?

Not Laravel-way, but usually when I need custom functionalities from other languages, such as:

• compiling Tesseract OCR or getting the binary https://github.com/tesseract-ocr/tesseract and downloading it locally to your project
• using the local Filesystem disk to store images temporarily after upload
• using Symfony Process to build the CLI command and run Tesseract OCR programmatically. Tesseract will output the OCR findings as a file that you can later read.
• Read the file after the CLI command finished with file_get_contents() (after the process finished)
• ??? profit

>La protezione più blanda è inviare una foto del testo invece che il file Word. Se è un testo molto lungo questo potrebbe essere sufficiente a dissuadere qualcuno dal riscriverlo manualmente o dal trovare app&co che elaborano il testo da un'immagine

https://github.com/tesseract-ocr/tesseract

L'ho usato per acquisire testo scritto da foto fatte col cellulare. Tipo tre minuti dal motore di ricerca al testo in blocco note.

>and automatic OCR

It's actually HWR - Handwriting Recognition. Specifically, OCR translates from bitmaps whereas HWR is given vector data and therefore automatically knows e.g. which direction a line was drawn in (as in, was the line drawn left-to-right, or right-to-left?), which may be hard or impossible to extract from a given bitmap.

OCR is in extremely widespread use (see: Tesseract) but HWR is relatively niche.

Hello u/CreativeMischief, I'm planning to add the crafting skill / reagent bonusses to the calculations soon. About the recipe data, I've got them from NWDB with the permission of the owner. The price data is from my server. I took screenshots and ran them through a tool I wrote (essentially a wrapper around Tesseract. Thank you for your feedback.

In this case I would go the low effort route: Use tesseract (https://github.com/tesseract-ocr/tesseract) to do the OCR part and "pdftotext" from the poppler utils to convert all PDFs to text. The quality should be fine. Works on Linux and most probably also natively on Windows.

I'm working on python.

My idea was the following:

1. Create Screenshots while you play.
2. Once finished with playing, start a python script.
3. Crop the Screenshots from the selected folder
4. OCR -> create File with extracted Data.

This could then be posted on Forums or even a Grail Tool for Offline Use.
So I'm more or less building based on
https://github.com/tesseract-ocr/tesseract

Not sure if you still need help but with a Linux shell script and tools like tesseract and ImageMagick this should be automatable.

If you think that could be helpful to you, I'd be willing to help. I don't touch Windows with a stick though. :D

So you have a picture with text in it and you want the text back without re typing it manually, right ? You could try using OCR. There's some online options but this one is open source and i've had good success with it.

I’ve had good experiences with EasyOCR, the most well-known OCR library is tesseract though, which is developed by Google.

OCR is a software thing. not a function of the hardware. You run the software on the image files. Same with compression... nothing to do with your scanner. Theses day you can easy fit 450 high res images on a usb stick. If it is a typed manuscript and pre scanned . I'll do it for 50c a page. How are your typing skills, you could always type it out again. Be a good way to become familiar with the document.

Tesseract is a pretty good Open Source package https://github.com/tesseract-ocr/tesseract

I wrote a solver in python for myself, initially as a brain dead brute force as a "path walker" that generates all possible paths (sequences) with a given length (buffer size) and checks each path against the sequences. Needless to say, it's slow as hell. With a matrix of size 7 and buffer size 8, it would generate millions of paths and take ~30s to go through all of them.

I added few optimizations since then:

1. Parallelized path walking by partitioning the problem space in N sub-spaces where N is the size of the matrix. Since starting points are always on row 0, each starting point (0, j) is a partition.
2. Stopped wasting iterations with first walking then solving. It should just check for solutions while walking.
3. Early exit as soon as it finds a path that solves all sequences. If there's no "solve-all" solution, then it goes through all possible paths.

With these, run time went down to ~3s.

In regards to OCR issues, which library are you using? I'm using tesseract, but it can be quite bad in detecting these simple ASCII characters. I had to manually maintain a list of "fixes" (translation of things the ocr got wrong into correct things) and this list keeps growing.

I also did not detect buffer size automatically, simply because I'm just running it in a command line by myself so it's way easier.

How did you solve automatically cropping the matrix and sequences images? It seems to change position depending of matrix size. My lazy solution was to have fixed coordinates for each matrix size (5, 6 and 7). So matrix size is also an input parameter for my solver.

I'm not a dev at all (Linux Sysadmin here) so I'll probably just use what I know for now: write a script to take screenshots at regular intervals, delete duplicates, and then process the image with Tesseract https://github.com/tesseract-ocr/tesseract

This guy does the same thing I'm thinking of, I'm impressed with how creative he got! https://waldo.jaquith.org/blog/2011/02/ocr-video/

Tensorflow attention_ocr looks relatively new you can check at below link

https://github.com/tensorflow/models/tree/master/research/attention_ocr

Tesseract’s latest ocr is based on LSTM model you can try it. When I tried couple of months ago I got decent results..

https://github.com/tesseract-ocr/tesseract

Sorry, I didn't see you reply until just now because you replied to the thread instead of my post.

What program did you use and do you have an example of the printed text online?

Apparently there a lot of Optical Character Recognition tools on the web. An open source engine is Tesseract OCR. You might have to look around a bit to find a program that uses that engine with Dutch language library or try to run it yourself from here.

I hope you can make it work.

Thanks! It can! You can run it with the language argument (see this, scroll down to the languages section) as described in my README.

Something like this maybe? Of course this one requires knowledge. But as I know, you can host a local server to make it work offline.

Just read the Wiki on the page and the instructions. Here i provided you the installation instructions. Alternatively a University in germany even compiled it for Windows.

Ahahah Fiz uso do Tesseract OCR, mas a verdade é que os resultados não foram grande coisa. Por vezes não consegue detectar, outras vezes confunde o 3 e o 5... Portanto acabou por haver algum trabalho manual a corrigir essas coisas.

Agora que tenho alguns dados de treino, eventualmente irei testar uma abordagem de deep learning para criar um modelo para automatizar o labelling para os restantes episodios de 2020.

Best way? Look at Tesseract source code and figure out how much training data is needed to get good results with it. This is state of the art OCR engine and you are unlikely to find anything better.

Simple way - use MNIST database, create a simple 3-4 layers fully connected neural network. Apply some data transformation to input (binarize it so it's just black or white, no shades of grey).

Not so simple way (you will most likely need Python and Keras) - start from MNIST database. Use a combination of 2D pooling layers and convolution layers on the input data rather than outright binarize it, should yield better results.

If what I have just said sounds like an alien language, start here:

https://www.coursera.org/learn/machine-learning

There are multiple weeks on this course specifically about neural networks and actually detecting digits. It's also a great introduction to machine learning. Just keep in mind that this course is more of theory and math than programming (in some weeks your whole assignments are just transforming 3-4 equations into code).

It does try to teach some basics but in all seriousness if you don't know what's a gradient, matrix and how to calculate a derivative you are not getting far. But this applies to machine learning as a whole since this field is applied math with computer being used as an oversized calculator.

In this forum post, Joao indicates that he can't modify what's doing the actual OCR, which leads me to believe he's using some off-the-shelf solution under the hood, probably <code>libtesseract</code>.

Tesseract does support Japanese, but you'd have to jump through the hoops of installing it (via Termux) and then interfacing with it via Termux's somewhat limited Tasker support.

Alternatively, Joao might see this and save the day. 🤞

tesseract seems to use language modeling for that, https://github.com/tesseract-ocr/tesseract/issues/654#issuecomment-274574951

It currently runs with [tesseract](https://github.com/tesseract-ocr/tesseract), which does not support handwriting out of the box, but I was thinking to replace it with google/microsoft api ocr service. It would be an easy extension/change

+1.
Ich habe einen Drucker mit Duplex-Scaneinheit. Alles was reinkommt wird reingelegt, geht auf meinen Server, wird mit ein paar Scripten bearbeitet und analysiert^* und in meine Nextcloud geschoben. Parallel gehts aber auch noch zu Paperless. Dann mit einer fortlaufenden Nummer versehen und eingeheftet.

Die Scripte optimieren den Scan (mit unpaper), machen OCR (mit tesseract), Mein Script vergibt automatisch die fortlaufende Nummer und versucht anhand vom Inhalt rauszufinden, ob es eine eingehende oder ausgehende Rechnung, ein Vertrag, eine Gehaltsabrechnung/etc, den Absender zu extrahieren und schiebt das in einen dementsprechenden Ordner. Zusätzlich arbeite ich gerade noch an einer regelmäßigen Email-Benachrichtigung, die mir einen Screenshot des Scans und die Entscheidung schickt und mich das ggf. gleich per Link in der Mail korrigieren lässt.
Außerdem will ich von fortlaufender Nummer weg zu einem Indexsystem, so dass ich das im Ordner auch sortiert hab.

I have had good results with googles open source OCR software. And it's very scriptable. I just store all my pdf files on my Mac. They are searchable using spotlight.

https://github.com/tesseract-ocr/tesseract

I'd hazard to guess that the OCR library only do numbers and European characters. Everything else it can't interpret correctly.

And it would seem, that also includes text that isn't on a plain white background.

This library using the tesseract OCR engine, Hindi is available as a character-set for it to recognize, though you'll have to install manually. More info on that here: https://github.com/tesseract-ocr/tesseract/wiki

Question is - how will it fare with two character-sets in the same image?! You may have to do some image-wizardry to create two images out of the one - one image with the hindi text and one with the english. Perfectly possible if this is to be used on standard formatted images such as that ID card.

Ocr is really unlikely unless is smart enough to roll his own rcnn. i doubt he was using ocr since hed needto be watching the video to time the pictures to feed into something like https://github.com/tesseract-ocr/tesseract/wiki

If the pictures are good you can probably use optical character recognition to have them translated into text.

Use one of these programs https://github.com/tesseract-ocr/tesseract/wiki/User-Projects-%E2%80%93-3rdParty

Working on a PDF to Latex converter. Just discovered Tesseract OCR (I'm not an OCR person so it's new to me) which has functions for a lot of my outlined approach. This week will probably be filled with me installing and playing with the library to get a feel for it and how I can use it in an ETL tool set.

If it's metadata text/ascii/unicode in the file and not text-stamp on image itself, use yara.

If it's text-stamp, you can OCR all files with tesseract and export to files where you can search further with anything like Notepad++ or grep.

Individually: Get this MCSA in SQL DB administration complete.

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For the team/department:

• Setup some kind of full text indexing / federated search solution on all network shares to identify documents with potential long term retention policies, liaise with the business unit that owns the documents and educate on how to use our eDRMS to maintain these documents.
  • Have been thinking about ElasticSearch with FSCrawler (including Tesseract OCR for Images with text).
  • Possibly host this in AWS, will need to engage with architecture team for feasibility
• Focus more on automation and categorisation for our eDRMS system.
• Work with other govt. agencies to support their transitions into the digital recordkeeping landscape.

Here is a list of gui frontends to tesseract: https://github.com/tesseract-ocr/tesseract/wiki/User-Projects-%E2%80%93-3rdParty

Interesting! I need to download the report and run some crosschecks. I know these kind of extracts well:

cet1tttifl llfilllillliliilfllllliillllllilllilli mtttet1ittl

Lot's more of those as well I see. Too many for a 35M report. Did it come with OCR meta or did you scrape it?

I have been using Tesseract for a while, I kinda have a trove of crappy PDF's that are horrible scans and OCR's, and this is one of my main challenges is somehow getting something useful out of them. Tesseract 4 is a game changer in this regard, but I am still working on filters to get better OCR:

https://github.com/tesseract-ocr/tesseract/wiki/4.0-with-LSTM

It's not totally clear from the post, but if it's just images with text in them that have the ranks you could just use OCR to extract the text from the image. And then just have simple logic to figure out who won based on the text data.

Tesseract is the most prominent open source OCR library and if you use Python this is a wrapper for Tesseract. If you're cool with the paid route I believe Google and AWS Rekognition also have simple API services for this.

Tesseract expects the config files exists in $TESSDATA_PREFIX/configs. If $TESSDATA_PREFIX/configs/pdf doesn't exist, one of the following option should work:

1. Use -c option: tesseract -l deu -c tessedit_create_pdf=1 test.png out
2. Save tessedit_create_pdf 1 as $TESSDATA_PREFIX/configs/pdf, then run tesseract

For future references: https://github.com/tesseract-ocr/tesseract/blob/2a1d238bd57bee4b8836862ecf9ae9acd3f56000/src/ccmain/tessedit.cpp#L52-L54

// Read a "config" file containing a set of variable, value pairs. // Searches the standard places: tessdata/configs, tessdata/tessconfigs // and also accepts a relative or absolute path name. void Tesseract::read_config_file(const char* filename, SetParamConstraint constraint) { STRING path = datadir; path += "configs/"; path += filename;

It matches the drop image with materials and characters templates. For some texts that are big enough, tesseract is used. I only recently started working on the parser. The Valentine event was the first event it was used.

Here is the repo. Discord where discussions happen.

I used tesseract! It’s a command line tool, but fairly easy to figure out. The documentation is quite thorough and helpful, but I did have to experiment with some flags once or twice before I got the right settings.

Ah ok, I wasn't aware of that so thank you.

Perhaps /u/Bokuzuken12 might have better luck with a tool using Tesseract. There is a list of 3rd party GUI projects making use of the Tesseract library here.

Yeah, too bad this is a hardsub. If that channel embedded it as soft dub, at least we can tranlate from Thai to English, probably messy as hell. But it's better than nothing.

&nbsp;
It's certainly possible to extract the hardsub, then we can use tesseract.
https://github.com/tesseract-ocr/tesseract
But, it will be even messier result, since the sub is not on solid background, tesseract would have a hard time distinguishing the thai character.
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So,..while we waiting someone kind enouigh to give rough translation, we could try the tesseract method, But i'm too lazy (and hungry to be honest) to extract and cropping the extracted frame.

There very well may be a command line interface - I found some things regarding "dispatch" and win32com, but didn't get much further than that. It does look like there is a javascript API, but I didn't see anything regarding OCR at a quick glance.

OCRmyPDF uses tesseract under the hood, which is an open source solution. Apparently it's good but not as good as proprietary options. I haven't used it myself.

https://github.com/tesseract-ocr/tesseract Je peux vous dev une application mobile android qui fait ce que vous proposez dans la description, (prise de photo d'un Document, OCR, comparaison avec un document scanné de la même façon présent dans une base de donnée, surlignage des différences. Mon devis s'élève à 30000€HT et 15% des parts de votre startup...

[Contact en MP pour devis plus détaillé prise de RDV]

We should be able to disable that (source)

>By default Tesseract is optimized to recognize sentences of words. If you're trying to recognize something else, like receipts, price lists, or codes, there are a few things you can do to improve the accuracy of your results, as well as double-checking that the appropriate segmentation method is selected.
>
>Disabling the dictionaries Tesseract uses should increase recognition if most of your text isn't dictionary words. They can be disabled by setting the both of the configuration variables load_system_dawg and load_freq_dawg to false.

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Great job on extracting the characters. The capital I is a 1 too though, as u/Sigbert noted, the rest looks good. I also found this repository on github that has the alphabet in a single png file. Might help.

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thats doubtful. tesseract isnt just some small open source project. Its still supported by google because its the foundation of most of the OCR tech in existence today. If its not working for you something is likely wrong with how its configured. or it may not recognized the font on the receipt. check out https://github.com/tesseract-ocr/tesseract/wiki/Fonts. Also tesseract repo is still actively maintained and the most recent release was 26 days ago.

The best OCR engine I know is Tesseract currently primarily maintained by Google, previously HP.

It doesn't have a graphical interface officially, but there are many third party projects that implement one.

1 bit is not grayscale. 1 bit (or binary) literally means a pixel is either (completely) white or black. You are converting to grayscale.

Fortunately for you, the images you are trying to use contain black only in the letters, so what you can do is to simply perform a binary threshold and make every pixel that is not black, white.

I don't know how to do it with Pillow though, haven't worked with it much.

You also might consider using Tesseract 4.0 alpha, which uses a completely new engine based on neural networks and is much more forgiving regarding the image type, it probably can detect the letters in the original images without hassle.

Be advised that it is still alpha, and things like tessedit_char_whitelist will be ignored (it probably will be ignored even in final, since the engine is completely different)

More info here: https://github.com/tesseract-ocr/tesseract/wiki/4.0-with-LSTM

There's a link to the setting config variables in the same paragraph.

https://github.com/tesseract-ocr/tesseract/wiki/ControlParams

It looks like youll have to configure this in the init params which are only available from the C++ interface. PyTesseract doesn't seem to have a way to do that built in.

You should slide on over to a C++ subreddit, and they'll probably be better equipped to help!

Yes, pretty much.

I've used https://github.com/tesseract-ocr/tesseract with https://github.com/madmaze/pytesseract for python bindings.

It works fairly well, and there are enough tutorials/examples for it to make doing basic OCR pretty easy.

I have used the .net wrapper for tesseract in the past, and it works relatively well.

I have been approaching the problem set using inferred information (screen format changes, switching views, color averages, movement detection, etc.) to find the start/end of matches, stage detection, screen location, and I am working on using OCR to read onscreen % info as well as player names.

I don't have any experience with convolutional neural nets, but that may very well be a better approach to this than what I'm doing.

Either way, keep up the good work!