Month: August 2020

Watch our video to learn how to set up Service Level Agreements in ABBYY FlexiCapture and the various elements involved in this process. Some of these elements include: queues, task counts, workflows, and time limits.

Hello. Today I’d like to discuss with you setting up service level agreements or what we refer to as SLAs within ABBYY FlexiCapture. Now the concept of an SLA in workflow management is that when we go into a queue, like what you see on my screen, certain items that have a higher priority or a higher SLA, or have an SLA that is close to even expiring would be worked prior to other traditional documents or documents with lower SLAs. And there’s a couple of things when we enable SLAs that happen to the interface of the product. First off, you get a “Process Warning Task Count” and an “Overdue Task Count”. The software, as items come close to approaching their SLA mark, they will be notified as a warning to an end user. And that’s what this count represents. And once a document or a batch reaches past the SLA amount, then we consider that an overdue document or batch. And hence why we call that an “Overdue Task Count”.

Now, at any point you can right click and explore the queue. And you can see here that I now have certain batches that have expiration dates on them and a status of expired. As they get closer to approaching an SLA, they may have different statuses of a warning status, but as they expire, the status is respectively updated. Now the concept is that once I get a task as an end user, so if I push my “Get Task” button, what I will do is I will receive a batch that has either an expired SLA or an SLA that is close to getting expired. So it controls really the round-robin approach of the queue. It gives us the ability to kind of reprioritize those SLA documents to a higher priority.

Now, in order to set this up, we go into our ABBYY FlexiCapture Project Setup Station, and we will update the workflow. When we get into a workflow, just make sure you remember that batch types have their own workflows. And what you’re currently looking at is my default workflow for the project. So you may need to enable this at a couple of different spots depending on your architecture of your project. But what I’ve done here is I’ve enabled processing time limits for each batch. And then I have now this button “Set Time Limit” that is available to us. When we click that button, you can see, we have certain settings of the SLA. We can tell the software to use a time limit, whether it be minutes, hours, or days. And to also issue a warning as documents get closer to reaching that SLA limit, we may want the software to trigger an automatic warning, or we may want to control that on our own by setting a static value for this warning.

The other option that we have then is the availability to set a time limit with a script, and this will open up our scripting engine within the solution. The interesting part about is that we can accommodate a lot of different business scenarios. So we can look up information in databases. We can call web services. We can set time limits based on business hours and maybe not just, you know, server time hours. So a lot of control that we get when we set this time limit with a script, but nonetheless, don’t forget that concept of this service level agreement or SLA is to change the priority of tasks or batches within the solution. Thank you so much for watching this video. If you have any questions, please reach out to us.

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Watch our video to Discover how ABBYY FlexiCapture can use the tools of classification, field extraction, machine learning, and image enhancement to help you capture receipts.

Hello. Today I’d like to show you receipt capture within ABBYY FlexiCapture. Now this is really special technology because it really encompasses the breadth of what we can do within FlexiCapture and all the neat tools that we have. Some of those tools include things like classification, field extraction, machine learning, image enhancement. All of this comes into play when we do a receipt capture project. So on the screen, you can see I have 11 different receipts and the software has extracted an expense type, a total, a vendor and potentially a ton of other things here. I’ll actually show you some of the samples. On the left here is the information that we’re extracting out of the box. Now, of course, just like any FlexiCapture project you can add, remove, modify, but here the software has determined this is a gas station bill, and you can see the other types of information that it’s captured here.

Just kind of continue showing some of these other samples. Here’s a toll bill. Here’s a hotel bill where the software has extracted things like line items as well. Here’s a gasoline bill. Now, sometimes when we look at receipts, it’s actually really helpful that the end user be able to see the original, because remember the technology will enhance the image so that it can read it and extract the best information that it can. But at any point, the end user has the ability to right click and see the original image. And this may be helpful just in context, as a human reading, a document, the original sometimes tells us something that maybe the other image performed by OCR doesn’t tell us. And you can kind of see some of those differences here. You can see the textured background. You can see the lighter text. And those sorts of things, where the software is enhancing that so that we get the best read of the document, but we don’t always sometimes as a human get the full context. So a lot of information here at our disposal.

I’ll just continue showing you some here. Here’s a restaurant bill. Here’s a retail bill. Here’s a parking bill. Toll road. We got a hotel bill as well. And you can see, this is the cool part about the technology is that it’s extracted all of this information literally out of the box and the next thing that applies is the machine learning. So at any point, if I need to teach the software or redirect the software to look for a specific field or fix it, the software will remember those changes so that the next time I process a bill or similar bill, the software will be able to extract that information for us. So that really, really helps receipt capture type of projects become more and more intelligent for us.

Then lastly, I’ll show you here just a car rental. You can see here, it’s not the prettiest image. But you can see here, we got most of that information extracted off of that bill. So receipt capture is really a hard thing for most technologies to do because most technologies really center on only one piece of technology, but ABBYY’s machine learning, image enhancement, classification and field extraction technology combined into one project is really shown very well within this receipt capture type of project. Hope you enjoyed this video. Thank you so much.

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