Turning Raw Image Data into Usable Inputs for AI Workflow

You know how the majority of artificial intelligence projects look fantastic on paper, but they fail to function when they are required to deal with real image-based data? That’s because raw image data is often messy. It’s full of noise, missing parts, wrong labels, and weird lighting. And if you throw that straight into an AI model, it’ll confuse it more than teach it.

A study by IBM found that around 80% of a data scientist’s time goes into cleaning and preparing data before it’s ever used for training. That’s how much raw stuff slows down the real work.

In this blog, we will walk you through how to take raw image data and turn it into something clean, labelled, and ready for an AI workflow. You’ll see how pros handle images, fix problems before training, and save time by automating a few smart steps.

Why Raw Image Data Can’t Be Used As it Is?

Raw image data means the pictures that come straight from a camera or scanner. Nothing is fixed in it. Every photo has its own size, light, color, and clarity. Some are bright, some dark, and some half-cut. It’s all mixed up. Many photos have noise. Some are too small or too large. Many are not labelled at all. Files are often saved in random formats without any structure. Because of this, the AI can’t understand what each image shows.

Also, the image data is in printed form and is not properly labelled or structured. For example, you have to process receipts from 10 different billing software. Each one has its own structure and details. So if you give all these images as it is, the AI system may be confused while processing these images. But if you process these images and make a clean file and label each bill properly, it will increase the accuracy of the tool. So, before using raw data, it has to be cleaned, structured, and labeled properly.

Steps to Turn Raw Image Data into AI-Ready Inputs

 Now, let’s look at the steps to extract data from images and convert it into structured inputs for the AI workflow. We will understand it with a real example. 

1) Collect and Organise the Images

The first step is to collect all the required data and organise the images properly. For example, the images may be scanned documents, handwritten notes, or transaction receipts. So arrange them according to the data on them or the image formats like JPG, PNG, etc. 

Let’s say you have to build an AI model that can tell the user about the food restaurants in a specific area. It can tell about which food item is available in which restaurant or from where they can eat good or cheap food. So first of all, you have to collect data, like the images of restaurant menus, deals or offers on the billboards, ads from social media, etc., then organise these images with respect to the restaurant names and image formats.

2) Clean and Preprocess Images

After collecting, the next thing is to prepare the images for accurate text extraction. Raw images often contain noise, shadows, or uneven brightness that make data extraction difficult. So, here, the goal is to make every photo clear and uniform.

You can do it manually using image editing tools or use a simple tool for this, like OpenCV. It’s free and easy to use. You can write a few lines of code to optimise all images. You may have to crop extra parts and adjust brightness and contrast for text and background differentiation and to remove blurry edges so text appears clear for extraction. 

3) Convert Image Data into Digital Text

Once the images are preprocessed and optimised, the next step is to convert image data into machine-readable text. You can do it manually by typing on the keyboard. But it is a hectic and time-consuming job. So a better way is to use an online image to text converter tool. This tool can efficiently scan and read the printed text on the images and then extract the data and convert it into machine-readable format in a couple of seconds. 

However, the accuracy of the extracted text depends upon multiple things, including image resolution, text font and language. So be careful and use the tool carefully. After extraction, save the text where you can easily edit it. 

4) Organise and Label the Data

When you have extracted text out of images, the next step is to paste it into an editable file, where you can organise and format it according to the requirements. If your data is in textual form, you can use a Google Doc or MS Word file, and if it is in tabular form, you can use Excel or Google Sheets. 

After that, you have to classify and label the data. For example, you have to arrange the restaurant names, meals available, price for each type, overall rating from the customer reviews, location, etc., so you have to make a table and arrange the data accordingly. Look at this example:

Next, add labels that help the AI understand relationships. For instance, “Best Meal” refers to quality and rating, “Cheap Meal” means lowest price in category, “Popular Dish” is about the most mentioned or highly rated item in an area or a restaurant, and so on.

5) Validate and Standardise the Data

This is a very important step. Because the output of your model depends directly on the input. So, check the accuracy of the extracted data before using it. Fix any OCR errors, remove duplicates, and maintain consistency in naming or formatting.

For example, make sure “Lunch” and “lunch” are standardised and the prices are in the same currency. Also, make sure that all menu items are properly linked to their restaurants. Clean and consistent data improves AI model performance.

6) Save in AI-Compatible Format and Integrate

Once the data is structured and verified, the next step is to save it in a format like CSV, JSON, or a database table that is compatible with the AI pipeline. These formats allow AI tools and scripts to easily read, analyse, and update the data without errors. It’s also good practice to use clear field names and consistent data types. After saving, connect the dataset to your AI workflow or model training pipeline.

This way, once restaurant data is saved properly, it can be used to train an AI that recommends the best or most affordable meal options based on user queries.

Build Smarter AI Solutions with AyaData

Aya Data works with organisations across industries, including healthcare, retail, utilities, agriculture, and more, to turn raw data into working AI systems that deliver results. From collecting and labeling data to developing and deploying advanced AI models, everything is handled with ultimate accuracy and expertise.

The team has strong expertise in computer vision, natural language processing, and statistical modeling, so whatever your idea is, they can shape it into a solid, working AI product.

Ready to make your operations smarter? Aya Data delivers AI solutions that convert your data into insights and results. Contact them to get started.

Wrapping it Up

The modern AI tools may read the text on images and work directly on it, but the accuracy of output is not guaranteed. When the input data is unstructured and unlabeled, even the smartest models can make mistakes when working on it. So, it is advisable first to process the images’ data, make it structured and labelled, and then give it to AI pipelines to train the model. In this way, you can get the best results from your AI model.

Article written by:

M. Azam is a seasoned digital marketing specialist writer with a strong focus on B2B and SaaS industries. He holds a Master of Science degree and has three years of digital marketing experience. He excels in SEO, SEM, AEO, and content marketing and is proficient in Google Analytics. Azam is passionate about data-driven marketing. He stays updated with tech trends and experiments with new techniques to help brands grow in competitive markets.