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The Cost of Image Recognition Software Development

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Image recognition software has become increasingly popular in the past decade, with many organizations using it to improve their customer experience and streamline their operations. But how much does image recognition software cost to develop?

The answer depends on a few different factors, such as the complexity of the project and the technology used. Let’s take a look at some of the key elements that will affect the development cost of an image recognition system.

AI-powered image recognition development has become a game changer for many industries, from retail to pharma. Businesses are struggling to adopt AI as early as possible to become at the top of the competition. Investment in image recognition software opens new possibilities, makes business more efficient, and diminishes the possible risks. 

Image recognition software - a brief overview

According to the report, the size of the image recognition market globally reached USD 23.82 billion in 2019 and is projected to reach USD 86.32 billion by 2027, exhibiting a CAGR of 17.6% during the forecast period. 

global image recognition market

Image recognition technology is used in various industries to validate data. Adopting AI technology in automotive, retail, healthcare, and education spheres allows businesses to raise their productivity and improve key business processes. It explains the latest boom of investments traced within the AI field. 

The value of image recognition software across industries

Let us briefly dwell upon the value of adopting image recognition system across industries: 

Healthcaremodern radiology diagnostic, chronic diseases, biometric recognition, ultrasound, magnetic resonance imaging .

Tourismself-service border checkups.

Automotivevehicle diagnostics, driving assistance, car damage detection, analysis of driversbehavior through face recognition. 

Retailpersonalization of content, detection of the desired goods through images, logo recognition. 

Fintech payment checkups using fingerprint and facial recognition, financial transaction analysis.

Such popularity of the technology is fueling the growth of the market. 

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Image recognition technology insights

The cost of developing an image recognition system will depend largely on what type of technology is used. Artificial intelligence (AI) and machine learning technologies are becoming increasingly common for image recognition projects due to their ability to accurately identify objects in photos or videos. 

However, these technologies can be expensive, so if you’re looking for a more budget-friendly option, you may want to consider using simpler technologies such as facial recognition or pattern matching algorithms.

Image recognition software presents Machine Learning models trained with appropriate structured data for selected images. These algorithms are supposed to recognize the selected features within the given images and are further used to gain the valuable desired outcomes 

image recognition software system

Due to the emergence of neural networks and deep learning algorithms, it has become possible to argue image recognition and detection. Artificial intelligence detects, recognizes, processes, and classifies pixels with the highest precision through training data. 

It is believed to be the most important feature that a person can possess to identify pictures. Numerous complex processes occur immediately in the brain, which seems relatively easy to understand. 

how image recognition works

In recent times, computers have been without such features. Nevertheless, the use of artificial intelligence and neural networks enabled the development of artificial intelligence and neural network development. These were the basis for comprehensive computer vision systems and their key component, image recognition. 

Panasonic corporation and Amazon offer specific APIs which utilize deep learning algorithms to enable quick AI recognition and visual searches.

Classification of the image recognition market

  • By component – hardware, software, and services. 
  • By technology – code recognition, facial recognition, object recognition, pattern recognition, and optical character recognition 
  • By app analysis – scanning and imaging, security and surveillance, image search, augmented reality, and marketing & advertising 
  • By deployment analysis – on-premise and cloud-based. 
  • By end-user analysis – BFSI, media and entertainment, retail and consumer goods, IT and telecommunication, government, healthcare, transportation, and logistics 

Image Recognition vs. Image Detection

Generally, image recognition is used to replace image detection. But technical differences exist. Image detection involves looking at an image and finding different items within that image 

Examples are face detection in which algorithms search faces in photographs and identify objects in the image. If the object detected is very important, it doesn’t matter 

Image detection aims to distinguish between two objects to determine how many separate entities exist in a picture. Consequently, a box is drawn around the individual piece of material. 

Why adopt image recognition technologies?

Key market players keep investing in AI technology to enhance their systemsaccuracy and successfully implement the latest tech innovation as virtual reality, artificial intelligence, and Machine Learning technologies. 

Lately, with the growth of tech innovations, we trace the massive growth in data generation, which makes businesses deal with big data. Image recognition program greatly analyzes big volumes of data, processes complex data sets, and provides exact user insights. 

Few insights about image recognition software development

If you are not a tech person, you still need to understand the tech side of building the image recognition app. It will allow you to avoid major stumbling blocks and additional expenses and optimize the development process. 

As was already mentioned, image recognition apps operate on neural networks, enabling object detection, identification, and recognition. This process is called deep learning. It can be of two types: 

supervised unsupervised learning
  • Supervised learning – compares the image with the existing database and detects whether it suits the category  
  • Unsupervised learning – image recognition technology detects numerous image characteristics. In combination with Machine Learning algorithms, the software tries to recognize objects. 

Both these types operate on Machine Learning algorithms. 

Examples of the best image recognition software

Google reverse image – which allows looking for images by inserting image URLs. 

Snapchat – if you need to quickly and easily find an image? You can upload the existing picture, take a new one, and use the app on your mobile device. 

Google Image search API – allows integrating Google image search into one’s app. 

Google Lens – allows users to search for information about objects captured in photos. It is based on identifying objects and comparing them with the available visual data. 

Amazon Recognition operates on the AWS platform, which has a powerful processing engine. You upload the image, and the app recognizes all the faces and objects. 

Kairos AI – an app that recognizes faces. It is used to recognize video feeds and images in social networks. It is often used for CRM systems or marketing management. 

AiPoly Visionimage recognition assistance for the blind. The app identifies the object and provides audio feedback. It can detect objects in photos, recognize documents, and even translate. 

The development process of an image recognition app

The development process also plays a big part in determining the cost of an image recognition system. If you’re building from scratch, it could take several months—or even years—to complete your project depending on its complexity. 

Alternatively, if you decide to use an existing platform such as Google Cloud Vision or Amazon Recognition, you can significantly reduce your costs since these platforms come pre-packaged with most of the necessary tools and features needed for image recognition projects.

The cost of your project depends very much on this choice. As you understand, building your app from scratch is time-consuming and requires more expenses. 

You will need to create your convolutional neural networks and hire a data expert to train the proper algorithms based on the relevant data. Image recognition AI and Machine Learning APIs allow for significant saving of project expenses. 

Using traditional Computer Vision

Typically, computer vision approaches for image recognition are a sequence (computer vision pipeline) of image filtering, segmentation, extraction, features, and classification. 

However, designing such pipelines requires deep computer vision or imaging knowledge, lots of development and testing, and manual parameter tweaking. Computer vision technology is very limited in scalability or ability to reuse it in varying scenarios/locations. 

Image Recognition vs. Computer Vision 

Image recognition feature is often confused with computer vision technology but is different. Image recognition often requires many different computer vision tasks, such as image identification, or image classification. 

Process of building the image recognition app

We have prepared a brief overview of the development process to provide you with a better understanding of what it takes to build an image recognition app. 

Define your requirements 

The business analytics and project manager will prepare a plan based on your business needs. They will orient you on the type of your app, technology stack, target audience, and the number of features the app should contain. 

Detailed planning of features  

Focus on the main features you want to integrate into your future app. Then, with the experts, choose the core ones to start designing your MVP. 

Detailed planning of features  

Focus on the main features you want to integrate into your future app. Then, with the experts, choose the core ones to start designing your MVP. 

Choose platforms and tools 

Now, it is time to focus on the tech aspects. You need to pick the tech stack and decide on the platform. You also need to decide whether mobile development, web development, or cross-platform app. 

Hire the needed specialists  

Probably, you already have an in-house team of developers, then you will need to find only the lacking specializes and benefit from hiring remote experts. Or you may need to hire an expert outsourcing team. 

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The real cost of image recognition software

Before looking for a team of experts who will build the image recognition app for you, it is important to understand the development cost. Here are the main factors that influence the cost of your app: 

  • The type of your image recognition app, programming language 
  • Whether it will be the development from scratch or the adoption of Image recognition API 
  • The number of features you want to incorporate into your app 
  • The type of team you will hire – its size, level of expertise, location of specialists, type of the team 

To secure the smooth development process and launch the feature-oriented, intelligent app you need to hire a high-end team. The standard team should consist of the following: 

  • Project manager  
  • Image recognition tech specialist 
  • The software engineers skilled in the chosen tech stack and specialized in the given platform (iOS, Android, cross-platform) 
  • Business Analytics 
  • QA team 

Another factor that affects the total cost is whether or not you need to hire third-party services such as data scientists, software developers, or designers. These experts can help make sure that your project is completed quickly and efficiently while ensuring that it meets all industry standards and requirements.

However, they typically charge higher rates than regular developers so this should be taken into account when calculating your overall budget.

Average project cost

The average project cost may vary from $10,000 to $500,000. Let us take a closer look and dwell upon the costs it takes on each stage of development. 

  • Business analytics involvement will cost you from $2,000 to $12,000 monthly. 
  • The cost of app design and MVP will depend on whether you have a brand book. Basically, the budget spent on app design will vary from $2,700 to $10,000. 
  • The cost of the coding (app architecture and functional development) stage will comply from $2,000 to $70,000. 
  • The budget for project management will take up to 20% of the overall app budget. It will cost approximately comprise from $2,000 to $13,000. 
  • The cost for hiring the data analyst will cost a minimum $1200 monthly. 
  • You also need to consider additional spendings, like server expenses, API integrations, adding extra features, marketing expenses, and maintenance costs. 

Building an image recognition app is a complex process. It is difficult to calculate the exact budget for software development. However, if you have a deeper understanding of the key nuances, it will be much easier to orient on the expenses and distribute the costs wisely. 

To conclude

Image recognition software development costs vary depending on a number of factors including the technology used, development process chosen, and any third-party services hired for assistance. It is important to carefully weigh all options before deciding which route is best for your organization’s specific needs and budget limitations. 

With careful planning and consideration, you can find an affordable solution that fits within your financial constraints while still delivering quality results for your customers or clients.


The costs involved in developing image recognition software will depend on the complexity of the project and the resources needed to create it. Typically, development costs will include licensing fees, labor and materials, hosting fees, maintenance fees, and occasional upgrades. 

Depending on the scope of your project and its complexity, developing image recognition software could take anywhere from a few weeks to several months. It also depends on how quickly you are able to gather any pre-existing data that may be used in your project.

There are a few ways you can try to reduce the cost associated with developing image recognition software.

One way is to try to use existing open source libraries or code snippets instead of writing everything from scratch. Additionally, you could look for ways to minimize or eliminate any unnecessary features that would add additional cost. Finally, using cloud computing services can help reduce hardware costs as well.

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