Converting Images to Searchable Documents
They say you can't get the full potential from your technical drawings while they're in a raster format.
But there are other valuable benefits to converting your images than just editing your drawings. What if your goal is to create a searchable database of the data held within your images? This is where technology like OCR can be a real game-changer.
If you convert text within your imagery to text strings, you can begin to catalogue your imagery into a searchable database. Once organised into such a system, one must search for a text string within the imagery, and the relevant image will appear. This efficiency level is possible when you use conversion software incorporating OCR's power.
In this blog, we'll explore how you can transform your images into versatile, editable and, most importantly, searchable documents. So let's get stuck in!
What is OCR, and how does it work?
OCR stands for Optical Character Recognition. It is the technology that allows computers to detect and highlight text within an image. Examples of its use include the cameras that police use to track number plates that rely on OCR, as does the software that enables law clerks to search for particular legal cases within a giant database.
There are several different techniques that OCR utilises, the two most common of which are pattern recognition and feature extraction. The former involves a computer searching an image and comparing the information to a collection of fonts, numbers and symbols it already has stored. While effective, this approach is limited because the OCR can only detect standard fonts like Times New Roman or its OCR-A.
On the other hand, feature extraction has vastly improved OCR technology's accuracy. Instead of matching similar letters, the computer looks for features it has learned to form a particular letter or number in the combination. It should recognise, for example, that a short horizontal line sitting on top of a long, vertical line makes a 'T'. Using this technique, a computer system that can retain multiple neural networks (which allow for deep learning) can even learn to recognise handwritten text!
Raster
Raster images are suitable for specific purposes. Suppose you want to store high-quality photographs, for example. In that case, TIFF files are handy because they support many colours and boast lossless compression—allowing images to retain their quality even after editing or compression.
The issue with text, however, is that raster images are made up of pixels. And that's it. Even if a raster image appears to contain text, for all intents and purposes (in other words, from a computer's perspective), the text is indistinguishable from the imagery because it's all just pixels. The text isn't really text, and thus, it isn't possible to search for these details within a raster image.
Moreover, data cannot be attached to particular elements of the file, and zooming in or changing the scale will reduce the overall image's quality. So, storing textual information in a raster format is a bad idea.
Vector
Vector images comprise distinct elements defined by a mathematical equation; users can edit or attach data to individual components (including text) of a technical drawing.
As vector text is recognised as such (distinct from the surrounding drawings), you can search through it as you would in any other document. There's also the option of attaching data to the text elements within vector images. For example, you may add metadata like 'page title' or 'draft number' to your drawings. Before maximising this potential versatility, you must convert the text in your images using OCR.
Why make searchable databases from your images?
Making your images searchable can save a considerable amount of time and effort. Imagine you have a large volume of patent drawings, for example. In such a case, storing them as raster images could be more efficient. What you have is just a collection of pixels—the images do not hold any useful information about their contents. How will you ever be able to locate the image that refers to, say, 'fig. 2' when needed?
Enter OCR. When you use OCR to convert the pixels in your image into vector text, you create a database of information related to the image. This information can then be searched for by users who may be faced with tens of thousands of images to scroll through.
More seriously, making your images searchable can also protect them legally. Take, for example, designs for products. If your work is patented, it needs to be documented and available for others to see so they don't infringe on your designs. So inventors working for companies like Nike ensure their patented designs are searchable through large online databases. Interested parties can find the images by searching on engines like Google Patents.
Aside from benefits to your workflow, like increased efficiency and organisation, making your images searchable can also be a savvy business decision. It's not just more straightforward for you to locate your work—depending on where you store it, it's also easier for others to find; this could be great for promoting your services and getting your name or brand out there.
