Optical Character Recognition Puns

Creating large datasets using OCR from GCP & AWS, then go through and clean up the data and import into our data model.

What is the best way to test this data's accuracy afterwards as my creative mind is blank outside of spot checking it manually against the original paperwork.

Hi folks,

If you heard anything about Optical Character Recognition (OCR) i.e. the process of extraction and conversion of handwritten or typed text from image, video, or scanned files, this article is for you. It goes into how OCR works and what problems it solves. Let's see how it's tied to computer vision and pattern recognition.

So the images I'm going to work with are super high quality, and I need to create my own ocr for this. All the text is going to be how the text is in a google doc. The fonts will be very similar to teach other, there may be underscores, or underlines, but otherwise the images will be super high quality. What would the best models/libraries/frameworks for this be?

For people who don't know what ocr is, its recognizing text in an image.

Even businesses that are heavily tech-oriented don’t need to know all the tiny details of the technology they’re adopting.

But knowing more always helps, right?

A lot of companies take Intelligent Document Processing(IDP) just as a repackaged version of Optical Character Recognition (OCR). This understanding hurts them when they choose a data extraction solution that isn’t a good fit for their use-case because of the limited understanding of different models. Almost 30% of businesses get rid of the automated data extraction solution adopted because it was not designed for them in the first place.

Hey there, so I am currently using an OCR to recognize characters of different texts but am currently analyzing a bilingual dictionary. My OCR model can't seem to analyze any of the IPA phonetic characters. I am using Tesseract OCR and have also tried Cloud Vision API. Are there any pre-trained models or existing software that can accomplish this?

https://preview.redd.it/7tjwrh4yst471.jpg?width=1256&format=pjpg&auto=webp&s=2d4e01bbcd0cc850176e41b1781f61be2ba2b46f

Started development of a mechanism to automatically detect the contents of documents related to foodstuffs and save them in a database by using optical character recognition technology for product registration in Release commerce. This allows you to take advantage of all the information your users have so far.

ReleaseProject Official site

https://release.co.jp/rel/

Im new to machine learning (emphasis on new) and recently wrote a basic MNIST classifier. I can understand how the mnist CNN figures out how "inaccurate" it is. Each image has its own label for example the image of a 2 is labelled 2 so the output layer will have 10 outputs and ideally you want the third one (counting from 0) to have the greatest activation. But how are training images for OCR models labelled?

Lets say I have millions of background images each with varying types of text overlayed on top of them. How exactly would I go about labelling these images to figure out how inaccurate my output is? For example if i had an image of a stop sign and I wanted the model to extract the word "stop" from that image, I would label that image with the text "stop" and have a single output neuron correspond just to that word. But this gets complicated very quickly I would need equivalently millions of different output neurons each corresponding to the unique text in those images. The other way of doing it would be to have 26 output neurons each corresponding to recognizing one of the letters of the alphabet. But this model would not be able to extract text from images, it would only be able to tell you what characters are present in it.

So 1) how exactly is the dataset of an OCR model labelled, 2) how do we determine how "inaccurate" this OCR model how would you calculate how far off the model is from the label. 3) What does the output layer look like? how do we arrange the neurons to specifically pull out chunks of text from an image rather than just figuring out if a certain character is in that image or not? I dont think we can have each neuron correspond to a word and light up if that word is detected. There are lots of words and text combinations in our language besides the output layer would not be able to figure out which order those words were in.

After typing this all out I think Im confusing image classifiers with optical character recognizers this doesnt bring any new insight onto my questions however

What software can do OCR for engineering drawings? You wouldn't need it if you always had the DWG files, but a lot of times you have old scanned documents and PDFs.

Prime example right here.

Original Post (light mode): https://www.reddit.com/r/ProgrammerHumor/comments/hpd320/12_yrs_kubernetes_experience_part_2/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

Repost (dark mode): https://www.reddit.com/r/ProgrammerHumor/comments/hpw4r5/4_years_of_experience/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

The bot currently doesn’t detect this, but it is definitely a feature worth implementing. I have seen many false negatives as a result of the light/dark mode problem.

Even businesses that are heavily tech-oriented don’t need to know all the tiny details of the technology they’re adopting.

But knowing more always helps, right?

A lot of companies take Intelligent Document Processing(IDP) just as a repackaged version of Optical Character Recognition (OCR). This understanding hurts them when they choose a data extraction solution that isn’t a good fit for their use-case because of the limited understanding of different models. Almost 30% of businesses get rid of the automated data extraction solution adopted because it was not designed for them in the first place.

This article discusses Intelligent Document Processing(IDP) from different angles and perspectives and helps you determine whether it is a good fit for your business requirements -

https://docsumo.com/blog/intelligent-document-processing-idp