Nevertheless, the price is still visible and the OCR engine should manage to read it. Price tags are cropped from a larger image and the product description text is very small and blurry so we don’t expect to read it.
#Google ocr font full#
Resultsįirst, we analyze full price tag images where the price and product description are listed. Out – is the name of the file where the read text will be saved. We want to find as much text as possible so we use option 11. Files for English come with the installation.
#Google ocr font how to#
Take a look here to see how to install arbitrary language files. Here we used the Croatian language files. Uses specialized model designed for the specified language. We want to utilize the neural network only so we use 1. 1 is for LSTM (v4), 0 is for legacy and 2 is a combination of two. We won’t focus on explaining all features Tesseract offers but will only try to read price tags with a touch of options tweaking. Since the v4, they’ve added a deep neural net (LSTM) based OCR engine which is focused on line recognition, but also still supports the legacy Tesseract v3 which works by recognizing character patterns. It is maintained by Google which implies it is frequently updated and is well documented. Tesseract is an open sourced library that uses an OCR engine libtesseract and a command line program tesseract to recognize character patterns. Furthermore, we tested a model optimized for the Street View House Numbers dataset, coming from a paper published by Google. We tried the obvious two solutions when it comes to OCR - Tesseract and Google OCR API. Price tags are usually computer generated and have some structure so it seems that OCR algorithms would read them without a problem. You know that feeling when you think something is easy but then as you dive deeper into the subject, numerous complications pop out of nowhere? Well, reading price tags is like that. This blog will focus on the price reading part. The idea is to capture an image of a shelf as the one above, crop out price tags, read prices, and finally connect prices with adequate product types. Example of an image taken by a sales represetative. Fortunately for them, the magic exists and it comes in a form of deep learning and OCR. It would help if they could just take a picture of the shelf, and the magic would fill in all the product prices.
It’s a demanding and error-prone job which has to be done at least once per month. This is why companies pay big bucks for sales representatives who will visit stores and manually write down actual prices. Not all stores have online shops and even when they do, prices can vary significantly in each store, especially in stores geographically well apart. It may not seem obvious, but it is pretty hard to find real store prices. Why would you want to do this? Ask people from companies that put their products on shelves and they will immediately tell you: “We want to know how we fare price-wise against the competition”. Despite being long in existence, OCR is far from perfect because real world text can get pretty complex. Number plates, business cards info, old books, and traffic signs are just some examples where digital is still not feasible (although digital plates would be really cool) and in such cases, OCR comes into play. OCR is a widely used technology even today when digital almost always comes in front of analog. Here’s an image demonstrating OCR in action just in case the definition was too much for you. Thank you, Wiki, a bit too descriptive, but very accurate.
Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast). Many think OCR solutions are already perfect and can be easily utilized for any kind of text recognition task.