Government Technology (GovTech) is a rapidly growing industry that seeks to use technological solutions to streamline government functions. From digital infrastructure to innovative applications, GovTech has been used in many countries to improve government processes and services. However, while the potential opportunities for GovTech are immense, one major challenge that remains is the reliance on paper-based documents. This can be particularly true for governments whose systems are not yet digitized or who rely on legacy paper-based systems.
The government is one of the biggest users of paper documents, and most of those documents are created manually. This generates a massive amount of data that needs to be entered into various systems manually, which is time-consuming and error-prone.
Consider the average day of an administrative staff member in a government agency who, let's say, interacts with citizens who require government services.
They handle a huge quantity of paper and plastic papers, including passports, lease agreements, Tax IDs, driver's licenses, and many others. These papers are typically not in great condition. They might be tattered, outdated, filthy, challenging to read, or even impossible to generate a standard copy. Bills have very small, difficult-to-read print, and plastic cards reflect light.
Accuracy, precision, and patience are needed for this task. However, there are nearby individuals that discuss, inquire, and interrupt while feeling uneasy. This makes precision difficult, and accuracy sometimes calls for inhuman calm.
Why is Data Extraction a difficult problem to solve for Government Agencies?
While imagining the number of papers government agencies proceed with, we have to mention the immense variety of layouts and the vast palette of documents which is hard to describe in one sentence.
But there should be a way out. After all, there are programs that can extract text from a scanned document. True, there are many of them but…
The current market-available solutions cannot be trained to handle issues of this diversity and difficulty because they are virtually entirely based on OCR methodology (OCR).
OCR has its limitations. Firstly, it’s not that accurate. Secondly, it is not trained to extract particular fields and analyze document structure - that’s why using it to extract certain fields from documents of different layouts is challenging. OCR is unable to tell the difference between sender and recipient, payer and buyer, personal tax id and company registration number.
It is possible to build a solution using OCR outputs, but each new document layout, each new case needs to be handled programmatically, and requires human supervision - time and money.
Over more straightforward OCR techniques, the Tensorway approach that we propose here has numerous benefits. Our approach is fundamentally different, and in contrast to OCR it is trained to extract specific text fields. With this approach, our clients get the ability to extract any text fields from the documents written in many different languages. If required, our model may be rapidly tuned to function with new added languages.
The model has been trained to comprehend the text's logic, therefore the data will be successfully extracted even if the document is written in a different language but follows the same logic.
Different fields, languages, and document formats are all appropriately distinguished by our model!
Our model also pulls all relevant document information for any purpose. To further train it, we merely require the information from your organization. It is common knowledge that every business has procedures and ancillary software where data is saved and maintained.
However, because it works with both scanned PDFs and photos, our model may be even more useful.
Rarely do people photograph their certificates and other paperwork under favorable circumstances. You name it: distortions, poor lighting, several documents in a single image, rotated photos.
Correctly extracting information from photos is extremely challenging for OCR algorithms. They become stuck when the photos are slanted, rotated, or have multiple documents on a single page. Our methodology addresses all of these issues as well as even greater ones, so get in touch with us to find out how you can apply our approach to your process.
Developmental capacities of Anadea
Regardless of your level of technical expertise, the Anadea software engineering team will work with you to find the best route to your desired software solution. In the IT industry, Anadea engineers are recognized as a company for highly difficult projects and ground-breaking concepts.
Since its founding in 2000, Anadea has developed into a reputable provider of custom software development services on a worldwide scale. Its purpose is to collaborate with clients on software products that will meet their business objectives in a respectful and open manner.
Professionals from Anadea will provide whatever assistance is needed for the integration of your current system with the Tensorway AI solution. Here, you can also create a software solution from scratch for government organizations in order to use Tensorway's white-label AI solution for Data Extraction in the most effective way possible.
Data extraction using AI refers to the automatic identification and extraction of relevant information from unstructured or semi-structured data sources, such as text documents or images.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is a method for recognizing and reading text in images with Computer Vision technology.
White Label AI
In the AI world, white label AI solutions are ready-made solutions sold under different brand names.