How AI Improves OCR Accuracy Over Time

Optical character recognition (OCR) is an integral part of any document processing automation system. The technology uses a combination of image-capturing hardware such as scanners, and software to convert PDFs, printed, or handwritten texts into machine-readable codes. These codes are then processed to produce editable and searchable digital archives. As organizations increasingly move toward digitalization and document automation, the accuracy of OCR becomes critical. The introduction of artificial intelligence has significantly enhanced traditional OCR capabilities, ensuring AI OCR accuracy. AI-driven models allow businesses to recognize complex layouts, varied fonts, and even low-quality scans with greater accuracy—making document automation faster, smarter, and more reliable than ever.

Traditional OCR Limitations

Although Optical Character Recognition (OCR) is a very important tool in document automation, there has been concerns regarding the accuracy of traditional OCR. It faces challenges in recognizing illegible handwriting, skewed fonts, and has trouble processing other elements like tables and noisy images.

The following are the limitations of traditional OCR:

• Accuracy: The accuracy of the OCR output drastically goes down while dealing with handwritten documents, intricate layouts, or skewed fonts and texts. Reading poor quality images and text in fonts which are lesser than standard-size is also a challenge.
• Multi-languages and Fonts: The OCR platform uses pattern recognition algorithms to match the scanned texts and characters with the ones present in its database. Hence if the text image is in a different language, or multiple languages are involved, or fonts are not in the OCR’s database, it would produce inaccurate readings. Since the algorithm is not adaptive, it may fail to identify unique language symbols or misinterpret certain characters.
• Formatting Errors: Traditional OCR doesn’t have the capability to preserve the format of the original document. It won’t be able to process font styles, line breaks, tables, graphs and indentations. This results in the extracted document having misaligned text, erroneous spacing, incomprehensible tables and incorrect line breaks.
• Lack of Semantic and Contextual Understanding: While traditional OCR excels at character recognition, it fails to understand the nuances of a text passage and the relationships between the extracted data. For example, to a traditional OCR an invoice and a survey form are one and the same – just a text image to be converted to an editable digital format.
• Dependency on Image Quality: The accuracy of the extracted document using an OCR largely depends on the quality of the input image. Low-resolution, faded text, or poor lighting conditions can introduce noise that hinders accurate character recognition. Blurry or distorted images might cause the software to misinterpret characters, leading to transcription errors and requiring manual intervention to rectify discrepancies.
• Data Privacy: Because OCR works on a parameter basis, there is always the risk of it inadvertently uploading sensitive information, such as IDs, confidential documents, and financial data to the software provider’s server. This would then become susceptible to cyberattacks and security breaches. Hence it becomes necessary to redact and mask such information before putting the documents through the scanner. Also, companies need to ensure adherence to the GDPR and SOC-2 guidelines when it comes to storage and utilization of such information, failing which would attract harsh penalties.

These are but some of the limitations of the traditional OCR, and these can be addressed to a great extent by integrating the OCR software with proper AI tools.

Benefits of AI-powered OCR for Businesses

AI brings a sea-change to how traditional OCR works. Particularly, the machine learning algorithms of AI make the OCR smarter by allowing systems to learn and adapt based on patterns and features identified in data.

Role of Machine Learning in OCR Technology

The neural networks of ML algorithms can make significant improvements to the functioning of traditional OCR.

Let’s see how neural networks improve OCR performance:

• Neural Networks: Neural networks enable complex pattern recognition by mimicking the working of a human brain.
• Recurrent Neural Networks (RNN): This aids in understanding the context of characters within a word and is useful for sequence data.
• Support Vector Machines (SVM): This helps in finding optimal decision boundaries and is useful when it comes to classification tasks.
• Convolutional Neural Networks (CNN): With the capability for recognizing spatial patterns, CNN specializes in image-related tasks.
• Random Forests: This provides OCR with robustness and better accuracy with the help of ensembles of decision trees.

The following are some ways in which AI enhances OCR accuracy:

• Error Correction AI can ensure that the data extracted is accurate and reliable by detecting and correcting errors in real time. This is particularly important in industries where data accuracy is critical, such as healthcare and finance.
• Adaptive Learning AI algorithms reduce the need for manual intervention and increase the accuracy of data extraction by adapting to different fonts, handwriting styles, and document layouts.
• Traditional OCR struggles with noisy or low-quality scans, resulting in inaccuracies. AI’s ability to adapt and learn from different types of input data improves OCR’s capability to handle challenging scans.
• The multiple language capability of AI-powered OCR empowers the systems to handle multiple languages and scripts, making them more versatile and useful in a global context.
• The contextual understanding feature of AI OCR can understand the context of the text, which helps in disambiguating similar-looking characters and words. For example, it can differentiate between the letter ‘O’, and the number ‘0’ more efficiently than traditional OCR.

Conclusion
Accuracy of the data extracted from documents is crucial especially when it comes to industry-critical applications like invoice processing, legal contracts and others. An efficient AI-powered OCR not just makes life easier for you but also improves efficiency, and effectiveness of your data processing workflow.

DeepKnit AI integrates OCR’s data extraction power with advanced Machine Learning (ML) and Natural Language Processing (NLP) models to deliver intelligent automation that can efficiently manage diverse documents and images at once. Its OCR engine ensures accurate data capture, creating a strong foundation for the next phases of document processing.