This is the final article in our five-part series highlighting the advancements in data extraction solutions over the past two years. Through this series, we aim to demonstrate the progress made in recent months and showcase the capabilities that were previously unavailable. The most notable improvement lies in the ability of current solutions to learn autonomously.
This is the fourth article in our mini-series, which aims to showcase the evolution of Data extraction solutions over the past 2 to 3 years. These articles highlight the advancements made in recent months and illustrate what is now possible. The most significant improvement is the ability of current solutions to learn autonomously.
If you missed the introduction to this mini-series, you would find it here: Mini-series: The real power of new data extraction strategies. The previous article discussed how modern solutions could effectively handle repetitions on handwritten documents: Use Case 3 - Handling Repetitions.
Could you please sort your groceries before I scan them?
When you go shopping, purchasing the same item multiple times is not uncommon. Most probably, you drop your groceries in random order on the conveyor belt. As a result, these items will appear on the receipt in the order they were scanned. As a consumer, this has no consequence and probably no interest. However, for companies extracting data from receipts, these details matter. These companies are typically interested in knowing what you buy, in what quantity, and at what frequency. Grouping identical items is a fundamental part of their analysis.
Welcome to the third article of our mini-series on the recent evolution of data extraction solutions. This series aims to showcase the advancements made in the field over the past 2 to 3 years. This article will discuss one of the most remarkable improvements in recent data extraction solutions - their ability to learn autonomously.
Data extraction solutions have advanced significantly in the last 2 to 3 years, allowing us to do things today that were impossible just a few months ago. One of the most notable improvements is the ability of these recent solutions to understand the context of a document.
If you missed this mini-series introduction, you can find it here: Mini-series: the real power of new data extraction solutions.. In the previous article, we discussed how to handle date formats, which you can access here: Smart Date Formatting.
This second article will explore another real-world use case: ditto marks.
What are Ditto marks?
You may be wondering what a ditto mark is, but chances are, you already know it, not by that name. A ditto mark is a double quote character that indicates that the word or value above it should be repeated. It is commonly used in handwritten documents where the precious “copy-paste” does not exist.
Let's look at an example. When listing items o
Effective management of technical documents is crucial for the smooth operation, efficiency, and responsiveness of industrial companies. The quality and availability of information within these documents directly impact the performance of operations, maintenance, and engineering teams. However, managing technical documents has always been a complex and resource-intensive task. Fortunately, with the recent advancements in AI algorithms, a new era in technical document management has emerged, offering higher data quality, faster access to information, and ultimately, more efficient operations and robust internal processes.
Are your mailroom processes automated but still not getting the desired results? Learn why deep neural networks power the new recognition engines for Digital Mailroom 2.0.
Exciting times ahead!
One of Moonoia's top projects has been shortlisted for the "Digital Project of the Year 2017" competition organized by Data News, Belgium's reference magazine for the ICT industry. Together with the "CIO of the Year", the winning project will be announced during the award ceremony which will take place in Brussels on October 25th. Fingers crossed!
