I am working on a project that requires character recognition as a part of it. I am using a handwriting dataset by IAM, so all the images are more or less taken in the same conditions. I am using pictures of words that have been provided by the dataset and following these steps
Binarizing and thresholding
Dividing the word into the characters constituting it
Resizing the extracted character
Letting tesseract figure out what the English alphabet is
What I'm trying to achieve is to store characters of a person's document in folders categorized by the alphabet and maybe form a template from them later on. For this I need to know which character it is.
Here's what I get as a result -
All the characters are properly segmented (for most cases). This is more of a tesseract question than it is a python question, but I'm using python to write the script and calling tesseract through the pytesseract wrapper.
I'm using OpenCV to manipulate the image. Images of these letter matrices are sent as input to tesseract (handled by pytesseract). The input is not an issue, I assure you. Is there anything else I need to do for tesseract to work?
None of these characters are recognized.
Tesseract doesn't support handwritten text well. You should try either ABBYY OCR for that or alternative free libraries like Lipi Toolkit.
Related
I'm trying to read the following images using pyTessaract.
The program I've made extracts these images from a video, I want to use pytessaract to extract text from them. The program also has the option to make boxes at the areas I want to the OCR to look at, it then crops the image at those places and runs OCR.
But still the output is not very accurate sometimes I get random letters / text included in the output, sometimes the text is completely random. It's just not detecting the text. How can I improve the detection ? Is there some preprocessing strategy I can use ? Or anything else ?
I'm trying to convert scanned images to text from tesseract ocr and it is working great except that my images has two languages in it and the tesseract is unable to detect both at once. I can either convert all the images to English (with Arabic being showed as some garbage value not roman Arabic), and vice versa if I convert it to Arabic (that is I get all the text in Arabic, with the English ones as Garbage).
I have tried to detect the exported text with langDetect but given the characters and ASCII are of English letters I'm unable to detect it.
I am sharing a sample of the image here, it would be great if someone can help me get a better solution of the issue.
Just Update your code with this
lang = 'eng+ara'
ara stands for ara.traineddata.
One more thing: arabic trained data might not be in the tesseract so download the ara.traineddata from git and paste it in tessdata folder of tesseract ocr.
I am also giving you the link for this traineddata: link.
I am trying to extract logo from the PDFs.
I am applying GaussianBlur, finding the contours and extracting only image. But Tesseract cannot read the text from that Image?
Removing the frame around the letters often helps tesseract recognize texts better. So, if you try your script with the following image, you'll have a better chance of reading the logo.
With that said, you might ask how you could achieve this for this logo and other logos in a similar fashion. I could think of a few ways off the top of my head but I think the most generic solution is likely to be a pipeline where text detection algorithms and OCR are combined.
Thus, you might want to check out this repository that provides a text detection algorithm based on R-CNN.
You can also step up your tesseract game by applying a few different image pre-processing techniques. I've recently written a pretty simple guide to Tesseract and some image pre-processing techniques. In case you'd like to check them out, here I'm sharing the links with you:
Getting started with Tesseract - Part I: Introduction
Getting started with Tesseract - Part II: Image Pre-processing
However, you're also interested in this particular logo, or font, you can also try training tesseract with this font by following the instructions given here.
Here is the image after the Pre Processed of a water meter reading...
But whenever I am using tesseract to recognize the digits its not giving an appropriate output.
So, I want to extract/segment out the digits part only as an region of Interest and to save it in a new image file, such that the tesseract can recognize it properly...
I am able to remove those extra noises in an image, that's why I am using this option.
Is there any way to do that ?
The Unprocessed Image is
Before you try extracting your digits from this image, try to reduce your image size so that your digit size would be about 16 pixels height. Secondly, reduce your tesseract scanned characters whitelist to "0123456789", to avoid other characters like ",.;'/" and so on being scanned (that is quite common on this type of pictures). Lowering your image size should help tesseract to dump this noise and not scan in or mix it with digits. This method should not work by 100% on this kind of image for sure, but to clear this kind of noise would be a challenge withoud a doubt by other ways. Maybe you could try to provide us with unprocessed image if you have one, lets see what is possible then.
I'd like to interface an application by reading the text it displays.
I've had success in some applications when windows isn't doing any font smoothing by typing in a phrase manually, rendering it in all windows fonts, and finding a match - from there I can map each letter image to a letter by generating all letters in the font.
This won't work if any font smoothing is being done, though, either by Windows or by the application. What's the state of the art like in OCRing computer-generated text? It seems like it should be easier than breaking CAPTCHAs or OCRing scanned text. Where can I find resources about this? So far I've only found articles on CAPTCHA breaking or OCRing scanned text.
I prefer solutions easily accessible from Python, though if there's a good one in some other lang I'll do the work to interface it.
I'm not exactly sure what you mean, but I think just reading the text with an OCR program would work well.
Tesseract is amazingly accurate for scanned documents, so a specific font would be a breeze for it to read. Here's my Python OCR solution: Python OCR Module in Linux?.
But you could generate each character as an image and find the locations on the image. It (might) work, but I have no idea how accurate it would be with smoothing.