Overcoming the Challenges of Character Recognition Software with AI

Character recognition software is the core of day-to-day processes in any organization. One good way to understand its significance is by comparing it to the current digital trends. Companies are now hailing data as the new currency. With advancements in artificial intelligence and data science, gaining insights from data is now easier than ever. Since character recognition software helms the data collection, extraction, and analysis process, it should keep up with the speed of advancements. However, the current solutions in the market need to improve in accuracy and effectiveness. This post discusses the critical challenges of prevalent solutions and aims to provide a robust solution in their stead. Without further ado, here’s a synopsis of the current market. 

Character Recognition Software – An Overview

To put it simply, the character recognition process identifies text and printed files and then converts them into machine-readable documents. Critical use cases of this process include transforming paper data into digital files and document verification. For this purpose, character recognition software is now a staple in most private and government organizations. Optical character recognition (OCR) is the prevalent solution for this purpose. However, since AI is now transforming each industry in its wake, data collection also requires an upgrade. Even though OCR still enjoys a reasonable accuracy, it cannot keep up with the current developments. 

Challenges of OCR

As discussed, OCR is the most-used character recognition software for data extraction and document management. However, this technology still needs to improve on certain fronts. For instance, OCR can struggle with case-sensitive data containing different font styles. This problem arises due to the fixed nature of the OCR database. Hence, an operator must load a particular font style into the software to function correctly. Similarly, OCR cannot identify and translate documents with non-symmetrical DPI, such as fax. Although there are a few other challenges, these give a fair view of the need for better technology.

The Role of AI in Character Recognition Software

A report suggests that almost 80% of B2B transactions will be digital by 2030. Therefore, the world demands efficient character recognition software to keep up with the surging demand. Luckily, artificial intelligence and deep learning have gained a lot of headway in the past few years. Rightly so, the progress in these technologies allows for breakthrough advancements in all sectors of life and industry. Moreover, large language models (LLMs), a subset of deep learning, are now more capable of deciphering text. Therefore, it was only a short duration before AI revolutionized document management as well. Using AI in this field allowed the creation of ICR – intelligent character recognition. 

Benefits of Intelligent Document Recognition

ICR is the next iteration of optical character recognition software. However, it is not a regular technological update. Instead, it marks the turning point in the revolution towards the use of artificial intelligence for quicker processing and better accuracy. In stark comparison with OCR, ICR is capable of identifying and translating handwritten text as well. Therefore, increasing its usability to an already greater level. Apart from this, AI behind ICR enables it to stay ahead of any changes in document styles and fonts. The learning capability of deep learning allows it this luxury. 

Multilingual Support

First of all, one critical flaw of OCR was its lack of dynamic language support. Even though it can understand multiple languages, a human resource is required to make it function. OCR comes pre-loaded with a handful of text fonts and languages. However, in a high-traffic environment, such as airports, it can hamper the smooth functioning of document management. On the other hand, ICR introduces dynamic language support in character recognition software. 

ICR Handwriting Recognition

As mentioned above, handwriting recognition is by far the most powerful capability of ICR. Since most industries today still use handwritten documents daily, ICR will be a significant boost. For instance, banks still spend a lot of human and monetary resources on the upkeep of check processes. On the other hand, ICR software allows them to convert scanned handwriting to text. Therefore, making it much more than a simple advancement in character recognition software. 

Summing it Up

Organizations using OCR justify its use by quoting the high initial investment of ICR. Yet, current development indicates that staying ahead of the competition is crucial. In terms of character recognition software, it comes to the price-to-benefit ratio. OCR might be cheaper, but ICR can save a lot of man-hours and resources in the long run. Also, the continuous learning ability of ICR makes it a much more viable option for future-proofing.