AI News, Detect Text (OCR)

Secret of Google Web-Based OCR Service

Optical Character Recognition (OCR) is one of the way to connect reality world and virtual word.

However, it is very challenge to achieve a very high accuracy due to lots of factors.

Talking about OCR, tesseract is one of the famous open source library that everyone can leverage it to execute OCR.

Tesseract 3.x model is old version while 4.x version is built by deep learning (LSTM).

First step is using Conventional Neural Network (CNN)-based model to detect and localised lines of text and generating a set of bounding boxes.

It is assumes that there is one 1 script per bounding box but allowing multiple scripts per image.

It does not only include character-based language model but also inception style optical model and custom decoding algorithm.

It defined as edit distance divided by reference length and scaled by 100.

I believe that it is due to neural network model already able to capture those characterise.

Powerful Image Analysis With Google Cloud Vision And Python

Bartosz is a backend developer at Merixstudio, a full-stack agile software team with HQ in Poznan, Poland.

The engine behind the API classifies images, detects objects, people’s faces, and recognizes printed words within images.

Another example is realtor.com, which uses the Vision API’s OCR to extract text from images of For Sale signs taken on a mobile app to provide more details on the property.

The higher quality data you deliver and the better the design of the model you use, the smarter outcome will be produced.

Download the file and add it’s path to environment variables: Alternatively, in development, you can support yourself with the from_serivce_account_json() method, which I’ll describe further in this article.

The response consists of detected words stored as description keys, their location on the image, and a language prediction.

For example, let’s take a closer look at the first word: As you can see, to filter text only, you need to get a description “on all the elements”.

If you look carefully, you can notice that the first element of the list contains all text detected in the image stored as a string, while the others are separated words.

As I’ve mentioned above, Google Cloud Vision it’s not only about recognizing text, but also it lets you discover faces, landmarks, image properties, and web connections.

You need to remember that you’re handing a photo over to a machine and although Google’s API utilizes models trained on huge datasets, it’s possible that it will return some unexpected and misleading results.

Some of them can be found funny, but there is a fine line between innocent and offensive mistakes, especially when a mistake concerns a human face.

With Python Library available, you can utilize it in any project based on the language, whether it’s a web application or a scientific project.

Google documentation provides some great ideas on how to apply the Vision API features in practice as well as gives you the possibility to learn more about the Machine Learning.

All that’s left to do is write a few lines of code, unwind your imagination, and experience the boundless potential of image analysis.

Kick off your Machine Learning project with Google Cloud

The fields of Artificial Intelligence and Machine Learning have opened up brand new opportunities for your project.

The cloud platform by Google is a set of tools dedicated for various actions, including: Google Cloud offers various Machine Learning tools which can extend your project with AI components easily.

The service brings its own huge database of already learnt words and that allows you to use the service immediately, without preparing any databases.

During the process of designing Machine Learning features, you have to consider many steps, including: Below you can find some benefits of choosing the Google Cloud Platform: What we can do using Google Cloud NLP: Real use cases - what we CAN do: Real use cases - what we CAN'T do: The examples below show what the Google Cloud Vision API is capable of.

What we can do using Google Cloud Vision API: Real use cases - what we CAN do: Real use cases - what we CAN'T do: Depending on your case, you can decide between running your own development of Machine Learning or using a third party service, such as the Google Cloud API.

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