In a world increasingly reliant on digital data, the ability to convert printed or handwritten documents into editable and searchable text is invaluable.
Optical Character Recognition (OCR) has long led the way in this data digitization race. Historically weighted down by inefficiencies, today’s OCR is on the verge of a new era, prompted by advances in Artificial Intelligence (AI) and Machine Learning (ML).
This blog post embarks on a journey through time, tracing OCR’s evolution from rule-based systems to sophisticated AI-driven engines reshaping the landscape.
The history of OCR
To simplify the Optical Character Recognition (OCR) concept, imagine a vendor sending the clients a physical invoice with their order. For the accounting data to be in order, the details of this invoice must be somehow “transferred” to the specific accounting system.
But how can this be done? This is where OCR’s magic comes into play: OCR’s ability to convert an image of text into a machine-readable format makes it possible to recognize and extract text for further processing.
The first OCR was created in 1974 by Kurzweil Computer Products, Inc., whose OCR could recognize text printed in virtually any font. This technology was also used to create a reading machine for the vision-impaired that read text aloud in a text-to-speech format.
The digitalization of historical newspapers in the 1990s made OCR a popular technology. Because the data was simple and followed the same pattern, the system was provided with templates to learn and recognize new information. This is how the OCR technology (called traditional OCR) we know today began.
From Rule-Based Recognition to Machine Learning
Traditional OCR will always be similar to template-based OCR. As the name suggests, it uses templates as a learning method to recognize and extract text from documents. The template-based algorithm reads only information that was previously learned.
Because of this, traditional OCR is used when dealing with documents that are structured, have a coherent layout, and are written in the same language.
Additionally, there are other limitations of this type of technology that need to be considered.
Limitations of the Traditional OCR models
Despite its disruptive role in digitizing text, traditional OCR technology is not foolproof. As we dive into the complexities, we’ll uncover a series of limitations that affect traditional OCR models:
- Lack of flexibility: Complex documents that don’t follow a consistent format or contain tables, images, or unusual formatting could be challenging for OCR.
- Error-prone: If characters are misinterpreted, errors can propagate through the extracted text.
- Formatting Challenges: OCR struggles with non-standard layouts, fonts, or handwritten text, affecting accuracy.
- Tedious setup process: Creating custom templates to train an OCR system is time-consuming.
While traditional OCR models still have their place, especially where structure and simplicity are needed, the industry gravitates toward more sophisticated, AI-driven OCR solutions.
Entering the Machine Learning Domain
Machine Learning (ML) is a subdivision of artificial intelligence (AI) that focuses on guiding machines to imitate human behaviors. ML can continuously learn, perform tasks autonomously, and improve performance and accuracy.
When ML meets OCR, an upgrade called machine learning OCR is created. This new way adds context to data, allowing OCR to handle different types of data and understand and recognize the general context of a document.
Moreover, Machine Learning allows OCR to identify new character versions and add them to its database for future comparisons. Slowly, it starts recognizing a wider range of characters, thus increasing its capacity to operate a high number of font types, handwriting styles, and unstructured data. This ability to evolve and contextualize sets ML-based OCR apart, allowing it to handle broad document types and qualities.
An example of an ML OCR is the Tesseract engine. Tesseract OCR incorporates advancements in machine learning and was developed by HP, in 1980. It was released as an open-source project under Google and it’s known now as “Google Tesseract OCR”.
The leap from static, template-based OCR to dynamic, learning OCR has expanded the possibilities for document digitization. Next, we’ll explore the benefits these advancements in OCR bring.
Benefits of the Machine Learning Models
Machine learning (ML) has augmented OCR technology with remarkable capabilities. Let’s explore the key benefits:
- Ability to process structured and unstructured data: If trained properly, Machine Learning OCR will understand and classify data and can classify documents based on the content and structure.
- Learning capabilities: ML allows computers to learn autonomously without human involvement, and, based on the experience the engine gained from other documents, the machine itself keeps on learning.
- Accuracy: Machine Learning OCR solutions can recognize patterns and then detect and extract data with an accuracy rate of more than 95%.
Based on the information above, it’s easy to think that machine learning OCR is the best. And, up to a certain point, it may be. But, as technology is constantly evolving, another type of technology is revealed: AI.
How AI is Transforming Text Recognition
Just one year after OCR was created, computer scientist John McCarthy held a workshop at Dartmouth on “artificial intelligence”. This was the first time the term was used, and AI became a mainstream idea fast. It was only natural to experiment and see which other technologies the power of AI improved. As a result, AI OCR was created.
AI OCR is a technology that uses ML algorithms to recognize and extract text from scanned documents, images, or PDFs. Thanks to the ability to transform the extracted text into a machine-readable format, further document processing is possible.
Through learning methods like deep learning or natural language processing, it transcends its predecessors by extracting text from imperfect, distorted, or diverse document types, providing a degree of flexibility and accuracy indispensable in the digital age.
Moreover, by learning from each new dataset, these systems continuously self-improve, pushing the boundaries of what automated text recognition can achieve.
Through this transformative process, machine learning has mitigated traditional OCR limitations and established new standards for flexibility and accuracy, ensuring that OCR technology remains an essential instrument of the modern digital era.
From healthcare to finance, AI-based OCR software delivers unmatched accuracy and adaptability. Their real-world applications are ample and growing; industries use them for tasks ranging from automating invoice processing to increasing the accessibility of print materials for the visually impaired. This highlights how AI-driven OCR creates real benefits and shapes a smarter OCR future.
Conclusion
The progression from rule-based to machine learning OCR is a testament to technological progress and the endless pursuit of efficiency and accuracy. Looking towards a future emphasized by digital innovation, AI will continue to mature. Its intersection with OCR marks a transformative chapter about how textual data is processed and interacted with. In this era of AI, OCR is an innovation sign, paving the path toward a fully digitized future.