At its core, Lighthouse is an idea we have been discussing in Connected Devices: can we build a device that will help people with partial or total vision disabilities? From there, we started a number of experiments. I figured out it was time to braindump some of them. Not very easy to read, right? Now, remember one thing: these pictures were taken by people who can use a camera correctly because, well, they can see the image.
Sufficiently hard that there are tracks in research conferences that deal with this kind of problem. So, we set out to benchmarkg existing APIs that claim that they can perform text recognition on images.
If you are curious, I invite you to try with the three samples above. The results are limited. In some cases, we get acceptable recognition. Using them comes at a cost, in terms of device bandwith, data plan, and actual per-request dollars. Bottom line These Magic Cloud APIs are acceptable for a demo, but are far from sufficient if we want to build a tool useful for an actual blind person. These days, the recommended OCR library in the open-source world seems to be Tesseract.
Testing it against any of the images above yields strictly nothing usable. This is not a surprise: Tesseract is fine-tuned for use in a scanner. Give it black letters, white paper, no textures, and a simple layout, and Tesseract will return text with an acceptable quality.
And fail. If Tesseract cannot be used on unclean text, we just need to clean up the text, using well-known or not-so-well-known Computer Vision algorithms, before feeding it to Tesseract. A quick look at Stack Overflow indicates that we are hardly the first project attempting to do this and that there are a number of solutions that should work.
If we succeed, we will have at hand a super-Tesseract, well-adapted to some categories of images. A super-Tesseract that we can further train to adapt it to the images in which we are interested. Say, text recognition on packaging, with the lighting conditions available in a supermarket.
During the past few weeks, I have been experimenting with algorithms for these things. All the problems are tricky, but I am making progress. So far, so good. Unfortunately, so far, these algorithms perform quite poorly, both in terms of speed and accuracy. Looking harder at SWT, I believe that this algorithm can be extended to perform the cleanup.
I am currently working such an extension, but it might be a few weeks before I can seriously test it. An algorithm called Deskewing is rumored to have good results. I need to investigate this further. It may be a problem with my samples, which are not sufficiently cleaned up at this stage, or with the algorithm, or even with me misunderstanding the results of the algorithm. Byproduct of the study: there are several very important pieces of information on boxes and clothes that would be useful for blind or vision-impaired users but that most likely cannot be treated by OCR.The quality of an OCR is crucial to the task of accurately extracting the information of interest, and the modeling of text-based classifiers, e.
The figure below shows the stages to achieve those goals, and our attempt to address the challenges stated above. A receipt is captured via a camera, and the image is passed to the Logo Recognizer of Retailer Recogniser in Information of Interest Extractorand the Text Line Localizerwhere the outputs, i.
The output of an OCR is a string of characters. One of the predicted results from both the Logo Recognizer and the Text-based Retailer Recognizer is selected. Both supports multiple languages. There are other OCRs out there which mostly are licensed. In other words, it locates lines of text in a natural image. It is a deep learning approach based on both recurrent neural network and convolutional network.
It is hope that when an image is broken up into smaller regions, before passing them into an OCR, it will help to boost the OCR performance. In a small set of samples, we find performance gain when feeding a whole image versus a sub-image into an OCR. The authors claimed that the algorithm works reliably on multi-scale and multi- language text without further post-processing and computationally efficient. Logo Recognizer This model recognises a retailer based on its logo.
In this example, we use Custom Vision to build a custom model for recognizer retailer based on how the receipt look. Custom Vision allows you to easily customize your own computer vision models that fit with your unique use case. It requires a couple of dozens of samples of labeled images for each classes. In this example, we train a model using the whole image receipts.
It is possible to only provide a specific region during training, e. When comes to prediction, either only the top region, or the whole receipt can be fed into the predictor. Figures below show the overal performance and some experimentation results with a small number of class, namely, rail 33bandq 14pizzaexpress 18walmart 34 and asda 26and respective number of samples uploaded to Custom Vision.
The table below shows some exemplar results. This model classifies well most of the time for receipts from known retailers, and able to distinguish a receipt from a non-receipt see row 6e. Note the confident probability socres shown. In row 2 and 4, there exist a test image with multiple bandq receipts and a test image with multiple walmart test receipts. This is just to show how the model will behave in corner cases like this. In practice, restrictions, such as onle one single receipt is allowed at one time, can be put in place.
Row 7 shows receipts from retailers that the model has no knowledge of. Unforturnately, the classifier is rather confused when a receipt which does not belong to any of the known classes.
To address the issue above, try adding a class called otherswhich will be a collection of receipts that has no logos, or any receipts that are not the intended 5 classes. How to decide between these two options will depend on the requirement specific to applications.
The figures below shows the performance of a different model which has the class others incorporated. In this example, 76 samples uploaded to Custom Vision for the model building. Figure below shows example cases. The first and third receipts are confidently classified as others. However, the test image in the middle, while it is predicted as others with the highest probability, the score for pizzaexpress is rather high too.Seriously, I have a huge folder full of memes and GIFs.
The issue is, the program I use to take screenshots, names each new file by the date and time the screenshot was taken. I have a screenshot folder each named by date and time.
I thus wanted a better way to go through my collection of memes.
My goal was to rename each screenshot file to the subtitles it contains. I have, in fact, been procrastinating this task for a long time now and the mid-year recess was the perfect time to start a small project. To read the subtitles out of my images, Google search led me to Tesseract. I started first experimenting with Tesseract and its wrapper, pytesseract in Python and used OpenCV for image processing and it worked perfectly right from the start! I started with a small Ruby script to read my images, process, and recognize the text in each one of them.
However, unfortunately, Ruby lacks in good tooling for computer vision or image processing. Next is a simpler version of the script I actually used.
The full script including the part where I process the output text and rename each file to the new name is a gist on GitHub. The first part of the script just loops through each file a x PNG image file in my source directory and reads it as a grayscale image.
First, we load it normally:. The second part is where all the image processing happens. Basically, the script negates the image to black and white to remove all the noise. This improves the ability of Tesseract to read the text in the image. This is what image looks like after processing.
You can see how almost all the details in the image are removed except for the vivid subtitles. In this part, we call Tesseract command on our image and get back our recognized text. As I mentioned earlier, I first started with a Python script to test Tesseract. Unfortunately, the Python version is much faster. I believe that most of the overhead in the Ruby version comes from using ImageMagick for image processing. The script is also on GitHub. The runtime became 3 times faster than what it used to be and, in fact, outperformed the Python version.
Tesseract wiki provides some tips to improve text recognition accuracy mainly, they are all about processing the source image before feeding it to Tesseract. This issue occurred with screenshots that contained subtitles in colors other than white.
If there are more than one screenshot with the same subtitles, one image file will overwrite the other. The Script Next is a simpler version of the script I actually used. MiniMagick :: Tool :: Magick. Oh, man. In my next life I'm coming back as a toilet brush.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.
If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. From time to time I receive emails from people trying to extract tabular data from PDFs.
I'm fine with that and I'm glad to help. However, some people think that pdftabextract is some kind of magic wand that automatically extracts the data they want by simply running one of the provided examples on their documents. This, in the very most cases, won't work. I want to clear up a few things that you should consider before using this software and before writing an email to me:.
Before these files can be processed they need to be converted to XML files in pdf2xml format. This is very simple -- see section below for instructions. After that you can view the extracted text boxes with the pdf2xml-viewer tool if you like. The pdf2xml format can be loaded and parsed with functions in the common submodule.
Recognize printed and handwritten text
Lines can be detected in the scanned images using the imgproc module. If the pages are skewed or rotated, this can be detected and fixed with methods from imgproc and functions in textboxes. Lines or text box positions can be clustered in order to detect table columns and rows using the clustering module. If your scanned pages are double pages, you will need to pre-process them with splitpages. An extensive tutorial was posted here and is derived from the Jupyter Notebook contained in the examples.
There are more use-cases and demonstrations in the examples directory. This package is available on PyPI and can be installed via pip: pip install pdftabextract. The requirements are listed in requirements. From this package we need the command pdftohtml and can create an XML file in pdf2xml format in the following way using the Terminal:. The arguments input. You can furthermore add the parameters -f n and -l n to set only a range of pages to be converted.
For usage and background information, please read my series of blog posts about data mining PDFs. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up.How to Extract Text from Image in Python?
Python Makefile. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit ce Oct 26, I want to clear up a few things that you should consider before using this software and before writing an email to me: pdftabextract is not an OCR optical character recognition software.We have been working on building a food recommendation system for some time and this phase involved getting the menu items from the menu images.
We want the menu items tp be in text format, so that we can easily track which restaurants are serving which dish and analyze the reviews to see which restaurant serves best. The very first apps which came to our mind when we thought about food were no other than zomato and burrp brownie points for those for whom these were the names echoing in their minds.
Extraction of text from image using tesseract-ocr engine
Zomato kept blocking our crawlers from time to time. So we found burrp to be a boon in this sense. So the obvious choice was to apply image processing techniques so as to extract the text inside these images.
We thought we would be getting okayish results using the tesseract-ocr engine for this purpose. We ran some of our images on it wihout any pre-processing and waited for the result.
But we were in for a rude shock.
Not only were we getting bad results but some of them were outright garbage text. But some results were turning out fine. Take for instance this image link. Result for this link. Now after some reading, we found out that grayscaling the images according would increase the OCR accuracy.
This improved the accuracy to a certain extent. Here is a sample greyscaled image for you link. Now tesseract was provinding a CLI interface for interacting with it. But how would you automate this? I am not gonna sit there and type. As always. I wrote a simple script which ran over the image directories, looping over each and every image for each hotel and ran tesseract-ocr on them.
Extraction of text from image using tesseract-ocr engine 04 Apr This post was long overdue! We want the menu items tp be in text format, so that we can easily track which restaurants are serving which dish and analyze the reviews to see which restaurant serves best Scrape them all! The first step was to scrape the images of the hotels.This article introduces how to setup the denpendicies and environment for using OCR technic to extract data from scanned PDF or image.
But for those scanned pdf, it is actually the image in essence. To extract the text from it, we need a little bit more complicated setup. In addition, it is easy for linux system but hard for windows system. We want to use pyocr to extract what we need.
And in order to use if correctly, we need the following important denpendencies. Note that PIL could use conda install pil. And also we need to setup the environment and path. First of all, do not change the default name of the folder, you can change the directory. But if you change the directory, you need to change some path setup from tesseract.
For the system path and environment, you need to add the directory of ghostscript, ImageMagick, tesseract-ocr into system path:. If your tesseract does not setup correctly, you will encount null value in this part, please carefully check the environment path setup. If the ghostscript does not setup correctly, this part will raise the error, usually I encounter : the system could not find the file. Basic package and software needed We want to use pyocr to extract what we need.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. A simple app PWA to extract text from images using Tesseract. No image upload. Everything runs locally on your device. Choose a image, edit the text if you must, then just copy and paste.
All credits from this app goes to the good people working on Tesseract. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. HTML Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Live version About No image upload. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Dec 28, Dec 27,