Do you still remember one of our recent articles where we talked about intelligent document processing? If not, please read it first.

Extracting text from an image is a technique that uses machine learning to extract text directly from an image without human assistance. How will the way we work change? How can extracting text from images using machine learning benefit modern business?

In general, thinking about extracting text from images is thinking about a way to teach AI algorithms to read. The first step in this task is to teach the algorithm to see the text (text recognition), and the next step is to process it and convert it to another form, such as a text file.

We will take a closer look at both of these steps in the text extraction process.

Optical Character Recognition for Text Recognition

The most common text recognition method, OCR, only produces excellent results in very specific use cases, but it is still considered difficult in general.

See Also: Differences Between Traditional OCR and AI

Optical Character Recognition is a technology that converts various types of documents, such as scanned paper documents, PDF files, or images scanned by digital cameras, into editable and searchable data.

Suppose we have a piece of paper, a certificate of secondary education. You can plug it into your computer with a scanning device, but you cannot edit it with tools such as MS Office tools.

You need much more advanced graphics software to change it. It takes time and skill.

If you want to extract and reuse data from this scanned document, you need OCR software that finds letters, inserts them into words, and then words into sentences.

This allows you to access and edit the contents of the document at the same time.

The most advanced OCR systems focus on reproducing natural human recognition. OCR systems are based on three basic rules: integrity, intentionality, and adaptability.

First, the observed object must always be viewed as an entity consisting of many interconnected parts. In our case, such an entity is a diploma.

Second, any interpretation of the data should always serve a purpose. Finally, the OCR program must be self-learning.

Extracting Images to Text in Chinese Characters with Machine Learning

At the end of the OCR part, we can move on to extracting text. You see, at the end of the first stage, we are left with an immutable image with text, not the text itself.

To solve this problem, the next step is to extract the text from the image. The localization process is carried out immediately after the text is recognized.

All characteristics related to a particular image are collected. Text mining, also known as keyword analysis, relies on machine learning to automatically scan text and extract relevant or basic words and phrases from unstructured data such as news articles, polls, and customer complaints.

See how it works below:

Text extraction and enhancement techniques are applied using machine learning algorithms. Finally, the extracted text is collected from the image and passed to a specified application or file of a specific type.