The reach of object recognition software stretches far beyond the CAD industry. This complex type of computer vision is useful to a range of fields—from defense to social media. In this article, we’ll explore what object recognition is, how it works and the variations in software that make it a useful tool in a number of different contexts.

Table of contents

• What is object recognition?
• How object recognition software works
  • Machine learning
  • Deep learning
• Industries that rely on object recognition software
  • Security and defense
  • Media
  • Tech
  • CAD
• Object recognition software

What is object recognition?

Object recognition refers to the process by which a computer is able to locate and comprehend an object in an image or video. It is important to distinguish this term from the similar action of object detection. The latter defines a computer’s ability to notice that an object is present. Object recognition takes this one step further and involves the system correctly identifying what the object in question is. 

Object recognition is important because object detection alone is often not sufficient. In CAD, for example, mere detection does not necessarily mean that said object can be subsequently edited for design purposes. Similarly, in the security industry, a program that is able to locate a human face is not half as useful as software that has the ability to locate a particular individual from a crowd of faces. 

In the most basic terms, object recognition software can discover, correctly identify and label objects within a video or image.

How object recognition software works

Humans experience recognition when we have a previous knowledge or understanding of something. This may be because we have been directly exposed to the thing in question before, or because we know of similar things that share its features. It is with this process in mind that computers are trained to carry out object recognition.

There are, of course, a number of approaches to this task. Different models are created depending on factors like the complexity of the subject material, time constraints of the job or how accurate the recognition needs to be. Software programmers must also account for the fact that issues like lighting, changing angles and obstruction of objects can hamper the success of object recognition. 

We’re going to explore two of the main approaches used to create object recognition software. Keep in mind that some industries will use a hybrid of both techniques.

Machine learning

Image source: Chatbotslife

This is one of the most common forms of object recognition software. It works on the basis of what is known as feature (or pattern) detection. You may be familiar with this term if you have explored the process of optical character recognition.

Machine learning involves a computer being provided with specific features that are associated with certain objects. It then stores these predefined rules and uses them to label new objects with which it is presented. The more varied the samples the system has access to, the more accurate the resulting software will be.

This kind of algorithm is based on an established set of rules. In other words, the system is not learning as it goes. Rather, it keeps referring back to the features it has been programmed to recognize.

Deep learning

Image source: Mike MacKenzie on Flickr

Deep learning is similar to machine learning in that it utilizes techniques like feature detection and feature extraction. However, rather than relying on one algorithm, deep learning involves the creation of a structure of layered algorithms called an artificial neural network. 

An ANN aims to produce a model that works very similarly to the human brain. In terms of object recognition, this means software that not only uses previous knowledge of features associated with certain objects to classify new ones, but also continually analyzes the new data it is given and uses this to draw new conclusions. So, in a way, it is learning as it goes.

Once it has been trained to recognize a particular set of features, the model builds on its collection of known features with every new piece of data it receives and begins to categorize them. When a sizable amount of data has been inputted, the software is able to make assumptions and predictions about new objects it has never been exposed to before. 

The deep learning process requires a lot of data, but the results tend to be highly accurate thanks to the complex abilities of the neural network.

Industries that rely on object recognition software

Security and defense

A rather obvious application of object recognition outside of the CAD world is in the security and defense sectors. Video surveillance has come leaps and bounds since basic CCTV was installed across the country. Now, thanks to sophisticated use of object recognition software, cameras can detect, identify and track moving objects. This could be used to locate the possible whereabouts of a missing person or, more controversially, target enemies in a conflict situation.

Object recognition is also incredibly useful when setting up high tech security systems. It allows for safety features like facial recognition scans and cameras that alert homeowners to intrusions on their property.

Media

When shopping around, you may notice that object recognition often comes as part of a collection of cognitive computer services wrapped up in an API (application programming interface). These packages are particularly useful for people working in media. For example, many of these APIs boast that their object recognition software has the ability to recognize celebrity faces. This would be useful for those working in journalism or marketing, as it enables companies to create searchable image libraries and categorize the pictures they have on file.

This type of object recognition is used by social media companies, too. Ever uploaded a picture to Facebook and it prompts you to tag friends that it has been able to detect? That’s object recognition at work. By detecting and extracting certain features, the program recognizes individuals that have been present in previous pictures (based on this prior knowledge), and labelled new images where it sees fit. Clever (and slightly creepy) stuff!

As for all you sports fans, it’s object recognition software that allows pundits to play around with sophisticated post-match analysis technology.  Take football as an example. Video analysis includes recognizing certain plays and “pathing” where the ball (object of interest) goes on the pitch. 

Tech

Image source: Automobile Italia on Flickr

It probably comes as no surprise to anyone that the tech world itself often employs object recognition for all kinds of projects. In fact, it constitutes the basis of most robotic vision. A particularly notable case that’s been in the news recently is driverless cars.

These systems require 3D object recognition in order to detect and comprehend objects like stop signs and pedestrians. You can see how the ability to go beyond mere detection and actually comprehend what an object is becomes important here, because stop signs and pedestrians require different responses. 

CAD

And, of course, we couldn’t wrap up without touching on object recognition’s contribution to the CAD industry! As we’ve mentioned, it comes in particularly useful during file conversions, as it ensures a higher level of accuracy.

You may find that some conversion software doesn’t come with object recognition. These programs might be sufficient if you just want to convert a basic logo. When it comes to more complex designs, though, you need to be careful and choose the right software. Some programs may not be able to identify and thus correctly convert some of the more complicated elements of a technical drawing like arcs or dash-lines. If you’re an architect or engineer, this simply won’t do. After all—professional results require professional software.

Object recognition software

As you can see, object recognition is an incredible tool that can be used for so many different purposes. No matter your field of work, there’s probably a way you could incorporate a version of the software into a project! In fact, if you’re so inclined, you can even create your own object recognition software. Programs like MATLAB provide you with the ability to do just that! 

We think we’ll just stick with Scan2CAD for now. If you’re looking to use object recognition with CAD, why not try out a 14 day free trial of Scan2CAD? Click on the box below and test out our features for yourself!

It would be no stretch to say that, without object recognition, it simply isn’t possible to achieve good-quality conversion results. That’s because object recognition tools discern how to map each object in your document onto the most suitable vector element. As a result, you’ll end up with a vector file that you can actually use and edit in CAD software.

To gain a better understanding of how converting a PDF to DWG with object recognition works, watch our video below. Don’t forget to read the full article for in-depth information on the conversion process!

Video: Converting a vector PDF to DWG with object recognition

Why convert from PDF to DWG?

If you work in any design-related field, you’ll have one goal in mind: creating something useful. The process behind this will involve multiple iterations of a single design, and, often, the repurposing of existing designs to create new ones. Designing something completely perfect in one try, with no reworking or editing, is nigh on impossible. Editing is essential to the process.

Unfortunately, PDF files, which are so common for document storage, are terrible at this. There’s a good reason why: their contents should look exactly the same on any machine. This makes them, in some ways, the digital form of printing out a definitive copy on paper. For this reason, they’re an ideal choice for sharing. However, it’s also why, if you want to edit them in any way, you’ll need to convert to another format.

If you’re working with CAD, one of the top formats to choose is DWG. The DWG file format has a long-standing association with AutoCAD, the market-leading CAD software from AutoCAD. However, there now exists a variety of ways to view DWG files without AutoCAD, with an array of competing software able to display the format.

Put simply, converting from PDF to DWG opens doors for designers. It’s what allows a user to turn a static image into a fully editable 3D digital model. From here, the possibilities are endless across fields as diverse as engineering, architecture and product design. Firstly, though, you’ll need to find a software that can convert PDF to DWG for you. As we’ll discuss next, this isn’t always the easiest task.

Why it’s important to convert PDF to DWG with object recognition

Without object recognition you cannot convert exploded vector shapes into their correct entities.

PDFs can be complex documents, with the ability to store vector, raster and text elements. DWGs, meanwhile, are vector files, and every element within them is editable. As a result, converting from PDF to DWG can be a tricky process.

Plenty of programs out there claim to be able to do a good job at this type of conversion. However, the results are often patchy. You can see an example of this in the video above, where the output image contains exploded vectors. This means that, instead of converting each element within an image to a suitable vector element, the conversion program has only drawn simple vector lines.

If you were unfamiliar with CAD, this may not seem so bad. After all, you’ve ended up with a vector image that looks like a faithful representation of the original PDF. Job done, right? Wrong. The kind of image seen at the start of the above video is next to useless for CAD work. The exploded text in the video above, for example, isn’t really text, but a series of lines. This means you can’t type over it—making editing annoying and difficult.

A good example of conversion output would separate these elements into vector text, circles, arcs, polylines, and so on. In order for conversion software to do this, it needs to have object recognition tools in its arsenal. These tools enable conversion software to discern which vector elements are the most suitable fit for parts of a raster image. In the video above, you can see just one example of this, with the conversion of vector lines to circles.

Raster and vector object recognition

When we think of object recognition, we usually think of raster images. However, given that PDFs can contain a range of different content, it’s worth noting that object recognition can encompass raster, vector and text.

In the raster-to-vector conversion process, this involves tracing appropriate vector elements over raster features. When this includes raster text, Optical Character Recognition, a separate, albeit similar, tool, can also come into play.

However, as we’ve seen, even a vector PDF won’t necessarily use the most suitable vector elements. The video above shows one of the more extreme examples of this, but other issues to watch out for may include a single dashed line being treated as a series of small, separate lines.

It’s easy to understand why this isn’t ideal when it comes to editing. Sadly, this is a very common problem with PDFs, which only support a limited range of vector elements. It’s for this reason that vector PDFs often replace complex elements, such as Bezier curves or arcs, with lines.

Object recognition can change this type of ‘dumb’ vector design into an image that you can edit with CAD software. However, not all conversion tools are able to do this well. That’s why choosing the right software is key.

Choosing software to convert PDF to DWG with object recognition

It’s clear why object recognition matters so much when converting from PDF to DWG. The trickier thing is figuring out which conversion software excels at object recognition, and which lags behind. Luckily, we have a few pointers you can follow.

Firstly, be wary of online converters. Though it doesn’t take long to find a PDF to DWG converter on Google, the quality of such tools is often extremely lacking. In fact, such programs are often unable to create anything more useful than the image in the video above! Other key pitfalls of online converters include safety issues, and threats to the security of your intellectual property. All in all, it’s just not worth the risk.

Some converters do manage to get halfway to a decent conversion job. However, they get sloppy when it comes to some of the finer points. Take text, for example: an average converter may do an admirable job of detecting raster text within your PDF and converting this to vector text. They fall down, though, when it comes to arranging that text into useful strings. Take a look at the image below to see what to aim for—and what to avoid.

On the left is an example of the unintuitive vector text other PDF-to-DWG converters produce. On the right is text converted by Scan2CAD and arranged into logical text strings.

The best solution, therefore, is to opt for a PDF-to-DWG conversion program with a track record of professional results. Enter Scan2CAD. With over 20 years in the business, we’ve learned a thing or two about what it takes to produce usable vector images. Read on to see what we have to offer.

Object recognition with raster images in Scan2CAD

As PDF files can contain both raster and vector images, you’ll need software that supports raster and vector object recognition. Scan2CAD’s advanced object recognition tools mean it’s one step ahead of other conversion software. Its recognition engine starts using preset vectorization settings; you also have the ability to alter these settings to suit your needs. In the video tutorial above, for example, you’ll can see how you can input personalized settings for features such as hatching, arrow lines, and dashed lines.

Additionally, Scan2CAD is able to combine object recognition with OCR. In fact, by running these two tools together on your PDF, you can obtain great vectorization results with distinct vector and text elements in just seconds.

Though ‘object recognition’ can have multiple meanings, within the context of Scan2CAD it refers to the process of recognising and transforming  elements within a raster or vector image to their appropriate elements. This includes distinguishing circles from arcs, text from lines, and so on. As you may be able to guess, this is one of the trickiest tasks for Scan2CAD to accomplish—and it’s also what sets us apart from other vectorization software.

In this post, we’ll run through what object recognition means in practical terms. We’ll also show you how it helps to improve your vectorization results—and what your files would look like without it.

Detection vs recognition

Image source: ShashiBellamkonda

Before we start exploring object recognition in earnest, we need to explore the differences between detection and recognition.

Writing on the subject, Intel suggests that the best way to think about these is as ‘discovery’ and ‘comprehension’. Detection, therefore is about discovering whether there are objects found within an image. Meanwhile, recognition aims to understand what the image represents.

In certain contexts, detection is sufficient. It is not, however, conducive to good results when vectorizing for CAD. Vectorization, after all, is about transforming a raster image into a usable vector file—and it’s the word usable which is key here.

It would be possible, for example, to simply convert a raster image into a series of vector polygons and lines. This may be fine if, for example, you simply wished to vectorize a logo for printing.

However, this would not satisfy the requirements of someone who wished to edit their design in CAD software. Take, for instance, the example of exploded vector text. Though this may initially seem to be a vector representation of raster text, it is not editable and not useful.

Reasons such as this are why object recognition is crucial. Any vectorization software worth its salt must aim not merely to detect what is present within a raster image, but to categorize its features with the aim of tracing appropriate vector elements over them. This is where object recognition techniques come into play—and where Scan2CAD excels.

The basics of object recognition

You may not be completely new to the concept of object recognition, you may have come across it before. In late 2016, Google released its Quick, Draw! game, which challenged users to draw six objects. The aim was to get its neural network to understand what each object looked like. You can see a screenshot of the game’s end screen below.

Google’s ‘Quick Draw’ recognition results. Test Google’s recognition ability here

Though a rather simplistic example, it gives you the gist of what object recognition is about. The aim is to pick up on an object’s defining characteristics in a visual sense. In the example above, this may include the camel’s hump or elephant’s trunk.

Types of object recognition

Quick, Draw! represents just one form of object recognition: one which relies on the field of machine learning. Many other forms of object recognition exist, and they control everything from the face recognition software at passport gates through to automatic number plate recognition.

Machine learning sits alongside deep learning as one of the two key object recognition techniques. The latter of these involves training a neural network to learn individual features of a given object using huge amounts of data.

Machine learning, meanwhile, involves selecting the relevant features in a given image, then feeding this information into a model. The model then uses this information to classify new objects. What both of these forms of learning have in common is the use of feature detection to classify objects.

Object recognition and vectorization

The principles governing the process of object recognition described above also apply to software such as Scan2CAD. Of course, Scan2CAD concerns itself not with distinguishing an elephant from a camel, but with correctly classifying different parts of a raster image with the aim of discerning which type of vector element is the best fit.

This is a trickier process than it may initially seem. For example, dash lines are a common vector element in CAD software. Telling apart a single dash line from a sequence of smaller lines, however, isn’t always easy. Arrow lines may pose similar problems, whilst other vectorization software may be unable to tell the difference between an arc and a Bezier curve (with many unable to process circles).

How object recognition works in Scan2CAD

Scan2CAD isn’t like other vectorization software—it’s a step ahead. That’s because our object recognition engine is adept at assigning the right vector entities to a 2D raster image. To do so, it uses preset vectorization settings as a starting point. This includes those for converting architectural drawings, mechanical and electrical drawings, CNC profiles, and more.

Where these settings come into play is in deciding how to categorize a specific object. Take the earlier example of a dash line, for example: one particular preset may decide that the spaces between each individual dash may be no more than 45px wide, while the dashes themselves must be no more than 50px long. The exact cut off points specified will vary by the preset you choose, and users also have the option to change these settings manually. This gives you the ability to control the object recognition process, ensuring you get the best results for your work.

Scan2CAD: object recognition in action

Need further proof of the effectiveness of Scan2CAD’s object recognition engine? Check out our video below. In this video, you’ll find information on Scan2CAD’s vectorization presets and how to edit individual settings manually. You’ll also see first-hand how Scan2CAD converts each part of a raster image into an appropriate vector element—and you’ll see what vectorization would look like if you don’t make use of proper object recognition techniques.

This video is taken from our series of Scan2CAD tutorials.

Why object recognition matters in CAD

Object recognition is clearly one of Scan2CAD’s strong points. But why does this matter for you as a CAD user? Well, without proper object recognition, you quite simply can’t create a usable vector file from a starting raster image.

Using Scan2CAD’s object recognition engine to accurately convert each element ensures you’ll end up with a finished vector image that you can export as a DXF or DWG, and then edit in your CAD software of choice. Without the techniques employed by Scan2CAD, you might be able to get a vector file—but it would be practically impossible to use.

Take the example of the floorplan we saw in the video above, which displayed doorways using arcs. If this were incorrectly converted into a Bezier curve, it may quickly become deformed, losing the meaning of the original image. Another program may even convert this into a series of small lines, which would be a nightmare to edit.

With Scan2CAD on your side, though, you needn’t worry about such issues. Thanks to our object recognition engine (and smart OCR techniques), you can be sure of professional results every time. It’s not for nothing that we call it the ultimate vectorization software!