AI technologies are here to stay and are making their marks on our daily lives. From personalized algorithms on your favorite streaming platform to finding the most suitable shows for you to curating the perfect social media feed filled with content that suits your preferences, they add value to our day-to-day.
Similarly, AI image processing is changing the way for businesses to improve their document processing workflows; getting the information extracted from images.
In this blog, we will go into detail about AI image processing, what it means, how it works its benefits to different workflows, and how it can help your business.
What is AI Image Processing?
AI image processing is the process or application of artificial intelligence algorithms to understand, interpret, and manipulate visual data or images. This also involves analyzing and enhancing image quality to extract information.
Essentially, the core functions of AI image processing such as image recognition, segmentation, and enhancement allow various systems to identify, understand, and classify images from a wide database.
Types of AI image processing
- Image Recognition and Classification: This involves training AI models to recognize and categorize objects within images and can be applied to include facial recognition, object detection, and image categorization.
- Image Segmentation: This involves dividing an image into segments to analyze specific regions independently.
- Image Enhancement: Utilizes AI algorithms to improve the quality of images by reducing noise, adjusting brightness and contrast, and enhancing sharpness.
- Forgery Detection: Focuses on identifying inconsistencies and irregularities in images, commonly applied in tasks like spotting fake IDs or document fraud
- Image Retrieval: Utilized AI to browse and search images from a large database of digital images that are similar to an original image.
How Does AI Image Processing Work?
AI image processing harnesses the powers of advanced AI algorithms and machine learning techniques to interpret the information it has been presented with. Here is how it works in a few steps.
- Data Collection: First, a large dataset of labeled images, relevant to the task needed to be performed, is collected. If the task was facial recognition, for example, this dataset would include images of faces and corresponding labels indicating the individuals. Check out the link for a free labeled data source.
- Recognition: Here, the AI model will begin spotting patterns in the images collected in the data set.
- Training the Model: The AI model, typically a neural network like a Convolutional Neural Network (CNN), is trained on this dataset. During training, the model learns to recognize patterns and features in the images that are associated with the provided labels.
- Feature Extraction: Now the trained model should be able to identify the important features in new, unseen images. AI algorithms, often based on deep learning models like Convolutional Neural Networks (CNNs), extract relevant features for facial recognition purposes for example, it might identify facial features like the eyes, nose, and mouth.
- Validation and Fine-Tuning: Think of this as a testing stage. A separate dataset of images (real and synthetic) will be created to monitor the models’ performance in recognizing features to prevent overfitting (when a model is trained too well to perform on a dataset and fails to perform equally on previously unseen images).
- Inference: At this stage, new images can be introduced to the trained model and using the previously learned patterns, should be able to make predictions. In facial recognition, the model might identify the person in the image based on facial features.
- Post-Processing and Visualization: At this stage, the model should be able to refine results.
- Learning and improvement: Once the fully trained model is ready and deployed, it will need to continue to be improved with cycles of retraining with new data to finetune the models’ performance based on user feedback.
This may be all very abstract to understand, so let’s break it down into some practical applications of AI image processing.
Practical applications of AI in image processing
- Image Enhancement in Photography and Video Editing: Machine Learning based image processing can be used to enhance the quality of images by reducing noise, increasing resolution, or improving color balance.
- Facial Recognition: Facial recognition algorithms analyze facial features, for identity verification purposes. For example, this is used in facial recognition when unlocking mobile devices but also on social media platforms like Facebook and Instagram automatically tag people in photos by recognizing faces.
- Object Detection: Object detection algorithms identify and locate specific objects in an image or video. This is valuable for tasks of road safety and hazard perception in self-driving cars.
- Reverse Image Search: Google reverse image search for example uses AI to analyze and compare visual content to provide the searcher with a similar or exact image. This showcases its ability to examine, identify sources, and discover related information, based on visual content.
These are only a few of the many task possibilities that can be performed using AI. The following sections will explore some practical uses of AI image processing in document-centric workflows.
AI Image Processing in Document Workflows
AI image processing and Optical Character Recognition (OCR) technology are often combined in document workflows. This begins with data capturing from the documents intended for processing either through scanning or digital uploads.
Once captured, the document images are processed to optimize their quality. Then OCR is applied, which allows the machine learning-based software to recognize and extract text from these images. The combination of these two technologies enables you to convert visual content into machine-readable text accurately.
Machine learning algorithms, then classify the documents based on content, layout, or structure. The document processing software then extracts relevant data from the text and images, utilizing Natural Language Processing (NLP) to understand context. Validation checks ensure accuracy, and AI facilitates workflow automation by intelligently routing documents based on classification and extracted information.
The processed documents and data can then be easily converted into structured data making it easier for them to be searched, stored, and sorted for a more efficient document management process.
Overall, AI image processing, and document-centric workflows can be easily optimized with automation to improve the accuracy of data analysis, and facilitate seamless collaboration within organizations!
How does AI-based image processing software work?
Machine learning image processing employs many advanced technologies for the analysis and information extraction of visual data. It can be visualized in some steps. Below, using the example of invoice processing, we will explain how each step of machine learning-based image processing works.
- Input: First an invoice or (scanned) document needs to be given to the software. The image can be captured mobile scanning device (via SDK for example). This may be a combination of text and visual elements. Using Large Language Models (LLMs) like LayoutLM, the software can easily understand and decipher the document and determine it is an invoice.
- Preprocessing: The software can perform, where necessary, preprocessing tasks on the invoice image, such as adjusting brightness, enhancing contrast, or cropping to ensure optimal conditions for analysis.
- OCR: OCR technology can be used to recognize and extract line items for example and other text from the invoice. This includes extracting information such as the merchant’s name, transaction date, items purchased, and the total amount.
- Text Extraction and Interpretation: The extracted text from the invoice can now be processed by the software to interpret its meaning. Here, Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER), may be applied to understand relationships between different pieces of information, such as associating a specific amount with a corresponding item description.
- Machine Learning Adaptation: Machine learning algorithms are at play to understand and adapt to the different layouts and formats of invoices which means that the system can learn from a diverse set of invoice images, improving its accuracy over time.
- Verification: The image processing software employs machine learning algorithms to verify the information extracted from the invoice. This can involve cross-referencing the extracted data through two-way matching to identify potential discrepancies or faults.
- Data Structuring: The software organizes the extracted information into a structured format, creating a digital representation of the invoice. This structured data can include details like itemized lists, prices, and dates.
- Output and Integration: The software is ready to export the processed data from the invoice and convert it into a business-ready format like JSON, TXT, CSV, and XML. The data can then be integrated into an accounting or expense management system for further processing and analysis.
Invoice processing is just one of the examples that AI image processing can support. In the next section, we will give more examples.
Functionalities and business applications of AI image processing
Image processing with AI solutions can bring many functionalities and help many businesses with:
Classification – Sort and categorize images or documents based on predefined criteria. For example, sort all the images of invoices in a separate folder from the images of receipts.
Data Extraction – Extract specific pieces of information from an image or document, such as names, dates, or numerical values.
Document Analysis – Analyzing the structure and content of documents to understand and retrieve information.
Text Recognition – The identification and extraction of text from images using OCR.
For businesses, this would mean the automation of various document-heavy processes including the following examples.
Healthcare Record Processing: Extraction of information from medical records to improve efficiency in healthcare administration.
Resume Parsing: Extracting relevant information from resumes, such as photos, skills, experience, and contact details, to streamline the recruitment process.
Price tag Scanning: Scan and extract information on price tags in stores for precise on-location data collection.
Document Verification: Scan and verify identity documents (passports, driver’s licenses, social security numbers, ID cards) for identity verification purposes in a range of industries.
Legal Document Parsing: Scan large volumes of legal documents to extract relevant data to improve and optimize work processes.
Financial Document Processing: Scan process and categorize documents including invoices, bank statements, receipts, payslips, and purchase orders to streamline accounts payable processes and financial data collection for example.
The Benefits of Image Processing with AI
- Streamline Operational Efficiency: With AI image processing, businesses can say goodbye to time-consuming manual tasks while handling large volumes of visual data more effectively through automation.
- Embrace Accuracy: Achieve higher levels of accuracy with AI image processing algorithms with tasks like object detection or document sorting.
- Automate Document Analysis: Automate image analysis with AI and reduce the need for human intervention. For example, AI can automatically improve the quality of an image for the best data extraction possible.
- Increase Team Output: Free up your team from mundane, repetitive image-related tasks with AI and redirect focus to more pressing and engaging projects enhancing overall productivity.
- Optimize Resources Dispersal: Save time and costs with AI image processing, by spending less time on manual image analysis or manipulation.
Now, we have laid out a lot of information, theoretical and practical exploring the uses of AI-based image processing in businesses and document processing workflows. How can you as a business take advantage of these technologies? By implementing intelligent document processing solutions like Klippa DocHorizon.
How to Get Started with AI-based Image Processing?
With Klippa, you can leverage an Intelligent Document Processing solution to automate manual processes of your workflow according to your needs.
Our solution integrates AI-driven image processing for easier extraction, conversion, and classification of documents. Additionally, you can anonymize and verify visual data to ensure that you adhere to industry regulations as well as detect and prevent document fraud.
If you want to create customizable workflows, check out our flow builder page or book a demo with our experts today!