Optical character recognition (OCR) extracts text from images. It outputs searchable, editable, machine-readable data. Its origins can be traced back to electronic reading devices developed in the early 1900s. Despite its long history, it hasn’t evolved significantly since its inception.
OCR has helped shepherd businesses into the digital age. However, its work is not done yet. What if, rather than simply reading text, it could comprehend context? With help from modern technology, it can move beyond pattern matching, pushing the boundaries of engineering.
Ongoing challenges facing OCR technology
OCR has difficulty assessing nonstandard inputs, including handwriting, cursive scripts, and stylised fonts. Complex structures — like tables and multicolumn layouts — can also confuse it. If the image is blurry, physically distorted, or poorly lit, its output will be riddled with errors.
Due to inherent technical limitations, a degree of error is likely. Given that traditional OCR tools have never achieved 100% accuracy, improvement is necessary. In applications where precision is crucial, this is a major problem. Thankfully, electronics engineers are already exploring ingenious solutions.
Emerging advancements in OCR technology
Conventional OCR scans an image pixel by pixel to identify patterns resembling letters and numbers. It matches them against a database of known characters or identifies the anatomy of typography — curves, intersections and loops — to determine which are present.
Software with natural language processing capabilities can analyse patterns more thoroughly, facilitating intelligent character recognition. An AI model is more adept at recognising structures and can even understand context, enabling it to extract relevant insights or format output correctly.
Another development in OCR technology is the use of Cloud-based systems, which use remote servers to convert text into machine-readable data. Users upload images to a service provider and receive the results within minutes.
Cloud-based tools are cost-efficient. Rather than buying hardware and hiring professionals to update software, companies can invest in software-as-a-service (SaaS) on a usage-based model. This approach lowers information technology overhead and up-front costs. It also facilitates automation, as centralised information is accessible anywhere and at any time.
Edge-based deployments run locally rather than on Cloud servers. This approach introduces resource distribution issues, but it is essential for real-time applications. It is ideal for companies guiding tourists or assisting visually impaired individuals.
Novel OCR solutions unlock new applications
Common applications for conventional OCR tools include invoice processing, record management, fraud detection, and application processing. Health care, retail, finance, and e-commerce sectors rely on this technology.
Modern OCR technologies may not be as flashy as AI, but they are quietly transforming many industries for the better. They go beyond simple scanning, automating entire workflows. For example, OCR can process thousands of mail items in digital mail rooms.
Mailrooms are an excellent example because they provide an indispensable, yet often overlooked, service. Traditional business practices are over half a century old, so companies are looking to move away from analog, manual processes. As digital mail centres become more common, OCR technology will replace conventional scanners and openers.
Soon, image and video analysis will be practical, expanding OCR capabilities beyond processing. Software will be able to recognise text in natural scenes. For example, it could read license plates on a toll road or track product labels on the production line.
Its integration with robotics and automation technology is another exciting venture. OCR could allow collaborative robots to read signs, displays and labels, enabling them to navigate and operate in human-centric environments. This would revolutionise manufacturing and distribution, which rely heavily on mobile robots and drones.
Hardware and system-level solutions needed
Major changes are only possible with hardware and system-level improvements, such as distributed computing, model optimisation, efficient data pipelines, and hardware-agnostic OCR.
Distributed computing
For large-scale document processing, tasks must be broken down into smaller, parallelisable subtasks that can run across a cluster of machines. This approach is particularly important for Cloud-based deployments.
Model optimisation
Data poisoning intentionally manipulates training data to influence a machine learning model’s output. Researchers have demonstrated that poisoning fewer than 100 samples in a large language model’s training dataset can corrupt an entire prompt. AI engineers must make their algorithms resilient to such tampering.
Performance optimisation is also necessary. Pruning removes unnecessary connections to reduce model size, while token compression decreases the number of visual tokens required for inference. The goal is to make processes faster and more memory efficient.
Efficient data pipelines
Optimising data flow facilitates high-performance intelligent character recognition processes. Professionals should focus on everything from image acquisition and pre-processing to feeding data to the OCR model.
Hardware-agnostic OCR
Tools should not be dependent on a specific vendor or technology stack. Having software libraries and frameworks that abstract the complexities of the underlying hardware makes OCR accessible to a broader range of developers.
Looking to the future of modern OCR systems
Modernising OCR systems entails ongoing research and development. The future of this technology lies in a multidisciplinary approach combining hardware, software, and systems engineering.
Devin Partida is the Editor-in-Chief of ReHack.com, and a freelance writer. Though she is interested in all kinds of technology topics, she has steadily increased her knowledge of niches such as biztech, medtech, fintech, IoT, and cybersecurity.
