Currently, we are living in a digital age where the exchange of information is done through soft-form documents. Often, these documents contain images. And sometimes, we need only data written integrated into those images out of a file. Therefore, most of us start copying text from the pictures onto a new page which takes a lot of time. That happens because most web users are unaware of text extraction techniques from images. These techniques can help in retrieving text from any image within seconds. These practices help in saving time.
In addition, their accuracy is far better than copying data manually. So, this blog post will shed light on different text extraction techniques from images. But, before jumping on to that, we will see the components of text extraction from images.
Text extraction techniques involve different steps to get accurate and exact results. The essential parts of text extraction processes are the following:
Image preprocessing is the first step toward the extraction of text from images. It constitutes the application of different methods to enhance the quality of images to improve the visibility of text before initiating further procedures. This may include resizing the images in which photos are brought to the optimum size to make extraction work perfectly.
In addition, it can also involve adjustment of contrast and balancing brightness level to achieve compact and compelling output. Image preprocessing aims to get the photos in the perfect shape for text extraction. That’s because the better the photo quality, the more accurate the extraction will be. Therefore, ensure you get your images in the best form to extract text from them.
Text detection is locating text inside an image to bind it by enclosing it in a rectangular box. Then, the extraction algorithms analyze an image to trace the areas that commonly show features related to text, such as color, texture, shape, etc.
Based on this analysis, the algorithms distinguish textual regions from non-textual elements. This step is the backbone for correct segmentation and succeeding processing. That’s because if the text is detected aptly, it will be retrieved correctly.
It is the process of determining where the text is inside an image and gathering it into text regions while removing as much background as possible. This step aims to accurately separate and isolate the individual characters and lines for further processing. Text localization seeks to focus and extract the needed text regions leaving behind unnecessary parts. This helps in effective processing to get a textual content from the image.
Up next, we will discuss the most common text extraction techniques from images:
Numerous approaches are being used to get text out of images. However, the most useful below are as they provide matchless accuracy and precision.
Optical character recognition (OCR) is a technique in which the text of an image is converted into machine-readable text format. Scanning an image with OCR technology saves the scan file as an image that can be used to edit text, which is impossible with digital photos. Usually, OCR algorithms keep various fonts and text image patterns to determine text inside images and convert them to machine-readable format. This is done by processing the letters inside an image to codes based on feature detection and pattern matching by detecting dark spots.
One prime example of OCR technology is an image to text converter. This technology is a perfect fit for document classification and data entry. You can quickly retrieve text from images and add them to a file to make the process efficient. Therefore, it has applications in businesses, the medicine industry, and security management. That’s because it improves the workflow and lowers the cost and error ratio.
The stroke width transform (SWT) technique is used for text extraction from natural images instead of digital images. It works by isolating connected shapes that formulate a corresponding stroke width. SWT first identifies the contrast edges in a snap. Then by navigating the image at each pixel edge in a normal to-the-edge direction until another standard edge is found, you can effectively determine strokes in an image.
The primary principle behind SWT is that text strokes usually have uniform width, and non-text regions show significant variations in the stroke width. Therefore, SWT distinguishes text from other image elements by segmenting consistent stroke widths. Furthermore, the output of this text extraction technique is better than OCR. Thus, it can detect text effectively in images with various orientations, font styles, and sizes.
Text extraction techniques are an unparalleled facility to have in the technological world. They help automate different procedures, reduce manual efforts, and save time. This includes enhancing productivity in finance, healthcare, and the administrative sector. Moreover, they can assist organizations and institutes in leveraging vast quantities of information within images to unlock insights and sharpen operational efficiency.