UiPath Document Understanding Reviews

My company has a confidential website where timesheets, salary statements, and other information regarding employee working hours are provided in PDF format. I plan to use UiPath's Doughnut Scatter and Keyword Classifier to extract specific data from these PDF documents.

The best feature, in my opinion, is, we can train the document using a machine-learning classifier. For example, if the PDF documents are of different sizes and formats, we can train all six templates with the Keyword Classifier and the Template. 

Currently, we have to train multiple templates because the column size and row size change in each PDF. My solution is to improve the process by training only one particular template so that the information can be extracted regardless of the row size and column size variations in the PDF. This way, we won't need to train each and every template.

I have been using the solution for two months.

The solution is stable.

The solution is scalable and this is the main reason we are using UiPath in our company.

The initial setup is straightforward and easy to understand.

I give the solution a nine out of ten.

We have a team of six developers that use the solution.

I recommend the solution. UiPath has recently added OCR capabilities to its Document Understanding feature, previously unavailable. This new feature is called UiPath OCR.

I use the solution in different ways. There are different ways to deal with the documents and extract data using OCRs, et cetera. I focus on document understanding with machine learning capabilities baked into it. 

I have worked on different types of documents. Both structured and unstructured documents as well as purchase orders, invoices, and time sheets as well. Depending on the type of the document or form, the solution would be different. If we have a structured document to retrieve the known information, then that would be easy. We would just use regular methods and simple extraction methods to get the data. However, if the documents are unstructured and the formats of those documents are different, that's where we would be using understanding and machine learning capabilities.

I've used it in healthcare, finance, and investments.

We're dealing with multiple vendors right now. When we deal with multiple vendors, each vendor has different structures for documents, and some of them provide data within papers while some of them provide just data as paragraphs. So for each of those types of documents, we have to extend the data based on the need.

How much the process is automated depends on the use case. It depends on the scenarios of the different types of formats of users. Sometimes there are different functionalities that we have to use within UiPath. If we're using Action Center, we probably would be able to automate almost all documents and that's where we would need users' input to validate the right information. In any case, we would be able to automate the majority of the documents since the bot would be able to expand the data. In such cases, the process might be longer. That's where we have to spend more time. However, if the documents are structured, it would be very easy for us to identify the data in those documents and then build the workflows.

The ability of UiPath to handle variant document formats, including handwriting is decent. Extracting the data is fine. However, it depends on what solution you implement and how much time you are ready to spend to implement that solution. If we have a plan to involve the Action Center within the solution, then that's where we would need a few inputs from the users to make sure that the automation is working fine, and that's where you would be able to achieve the majority of the success rate. However, if it is something that we just want to automate and we don't want to involve humans in it, then that's where we might result in a few exceptions as the data might not be right. That's where we would face some challenges. 

The machine learning capabilities have been quite fine. We've been able to digitize and classify documents and use them in our processes. That said, when compared to AI fabric, that's where we need to spend more time creating our own packages for it and then deploying those packages into the workflows. We need to make sure that we have a handle on all the documents.

I have found that 70% to 80% human validation is needed if we are trying to deal with sensitive data or if we are trying to deal with some confidential data. In those cases, we need to make sure that all the data is right. So as long as the document is structured and is well defined, and well-formatted, we might leave it 100% to automation. If any of these details are confidential or if any of these details require evaluation, then we will need user interaction. 

The validation process can be pretty quick. A code document doesn't take much time. It depends on the data. In any use case, I need to extract more than ten or 12 fields. If we're dealing with that number of fields, I'd estimate we need between two to 22 minutes.

The Average Handle Time, the AHT, depends on the cases. If there's no human involvement, then it would definitely take less time. If there's human involvement, the product could at least reduce the effort. The human involvement may drop from 20 to 30 steps down to one and they are just needed for validation. That scenario shows UiPath as a great time saver. Initially, we used to take 15 to 20 minutes to work on a document. Now, with automation, it might only take two to three minutes. It saves 65% to 75% in terms of time. 

There's still a chance of human error or tasks taking a few minutes as users would need to input some data into the document in the Action Center. That said, there is definitely a reduction and less of a chance of human errors over there. 

It's helped us free up time for other staff projects. That's the intention of implementing automation. Users can reduce their time on tedious tasks and focus on more important business needs. 

Document understanding works fine, however, it depends on what information you are providing it with. If the data is right, the data is good, however, in cases where the data is not right, it gets a bit difficult.

They could work on the digitizing and classification of documents. That would play a major role in document understanding since that's where we need to make sure that bots are able to extract data from multiple formats or multiple structures of the documents. The better they get at data extraction, the better we can automate. 

I've used the solution for two and a half years. 

I have worked on different use cases. There was one use case where I just worked on a similar type of document that had data entries for more than 300 to 400 users. I have worked with more than 400 to 500 documents of different formats and different sources. That's where I had to use machine learning packages. Right now, we are working on documents, with set formats and different structures, however, the volume of the documents is about 400 to 500.

They have a good technical team that supports the needs of the developers. 

Positive

It could actually take one year if you consider the effort which we had put into building that solution as well. We have a few developers working on it. We wouldn't see a return on investment within eight to ten months as we would just be starting with building the processes. 

We have witnessed some ROI. For example, it reduces review work from 60% to 70%. That's where the full-time employees would definitely save their time and can then focus on much more important business needs, which could help them get more projects or increase revenue as well. That's where you would see the most ROI. 

It definitely reduces the hours of work. The bots have the potential to also cover offline hours. 

While it depends on the use cases, the document understanding is good. I'd rate it eight out of ten.

The product is very, very nice. The tool relieves the data entry team from manipulating data. It shortens the time of extracting data by 70 to 80%.

UiPath should localize the built-in features for supporting Arabic scripts so we do not rely on third-party tools. It will take some investment. Sometimes, when we use third-party tools, we need to collect multiple samples of the same document in Arabic. It creates some errors in understanding. 

Sometimes, we need to do multiple formats for the same document. It happens due to the nature of the Arabic language. For the same document in English, we only supply one template. The output of handwritten documents in Arabic is very poor, regardless of the solution. For computer-printed documents, we need to tune the system.

I am using the solution currently.

The product is stable.

The tool is scalable.

We were using our own intelligent business processing solution. It is more convenient to use because it's our own product, and we rely on Amazon Web Services. It provides big advantages for Arabic scripts. It is more understandable.

The product is easy to implement to an extent. If we are familiar with the input, processing, output, and how to declare variables, it's easy. If we are new to UiPath, it would be somewhat difficult to understand. UiPath Academy and certifications are very important.

The difficulties in implementation depend on the environments, document sources, document types, and customer understanding. Sometimes, customers think the product can pass anything without configuration, which is not true in some cases. We have to be very clear with the customer about the type of documents and layout they can expect. Two working days are sufficient for configuring, extracting, and linking the process. One person is enough to deploy the solution.

The pricing depends on the context of the project. UiPath is a pricey tool for small customers. If the customer agrees on the cloud product, we discuss the cost accordingly. In some cases, even though the customer sees value in the product, return on investment, productivity, and enhancements of their processes, they decide not to choose UiPath due to budget constraints. They choose a different vendor.

Sometimes, the customer is small, but we could see the potential for using the tool because they might have multiple processes. The price we offer is based on the context and the size of the opportunity we get for references. Document Understanding has helped us automate processes like contract management, reconciliation of invoices against purchase orders, claims management, and HR processes.

We process 100,000 to 500,000 documents in a year. We also had a project for Dubai Customs to reconcile the customs clearance, which involved 500,000 to 1,000,000 documents. These documents contain tables, bar codes, dates, and checkboxes. Tables might span over multiple pages, and we must capture it completely. It becomes challenging sometimes. If there is an invoice of three pages, everything must be captured, but sometimes the values are inaccurate.

Around 70% of our organization’s documents are completely processed automatically. We haven't used the product for signatures. Customers often see from a productivity point of view. If their employees process ten documents an hour, and the tool processes 50 documents an hour, using the tool is an advantage for them.

The tool does not work for integrations. It extracts data. When we extract data successfully, we rely on other business tools for integration. Document Understanding serves as a first milestone in the process. The tool at the second milestone would pick up the extracted data and post it according to the processes or applications used.

We need human validation of Document Understanding outputs three out of ten times. It does not take more than 20 to 30 seconds per document. An employee would take ten documents per minute. RPA could handle 30 to 40 documents per minute. Document Understanding has helped us reduce human errors by 70% to 80%.

Document Understanding has helped free up staff’s time for other projects. From a development point of view, it has freed up 70% of staff's time. From an employee's point of view, it has freed up 80 to 90% of their time. We saw the value of the product after a couple of projects, especially when we implemented it and saw the value for ourselves. As a customer, vendor, or implementer, when we see the execution, we see the value.

If someone wants to take advantage of this technology, they must think multiple times about different scenarios. They must centralize the capture or the source of data. They can use any product that is easy to use and configure and link it to the processes. They must sync the templates and the configuration multiple times and link it to the automation strategy.

Overall, I rate the solution a five out of ten.

I work for an electronics company that deals with a lot of tedious tasks on a daily basis, such as processing PDFs from different vendors in different formats. Initially, we used a tool to extract this information for later processing. However, last year, we implemented UiPath Document Understanding with a self-learning model so that it could learn to identify all the fields even when the format changed.

We use UiPath Document Understanding to process purchase orders and invoices that are in PDF format.

We process PDFs in many languages, and UiPath Document Understanding can extract data from thousands of PDFs for our partners with high accuracy.

The AI and machine learning model has helped to solve many of the inaccuracies in our PDF data extraction, and it will continue to improve.

UiPath Document Understanding has helped reduce the amount of manual intervention and helped scale up the number of documents going through the process with over 600 partners in production.

Out of 200 documents processed each day, 50 undergo human validation. In most cases, manual validation takes under two minutes to review two fields in a document. More complex cases with errors in multiple line items may take five minutes to validate, but we prioritize these cases and train the model to improve its accuracy in the future.

UiPath Document Understanding helps reduce 40 percent of human error. Although we do encounter errors with the solution when the PDF is not clear or when it sometimes swaps the day and year on documents, overall the solution has helped correct many human errors.

Once we implemented the right methods we started to see value in Document Understanding immediately.

UiPath Document Understanding is user-friendly, with an easy-to-use self-trained model, and the OCR it provides does a good job even with scanned PDFs.

Every PDF contains simple fields, such as header fields, and line fields that are three to five lines long. Sometimes, a line field contains multiple fields, like a table within a table. Document Understanding cannot extract this type of data. We are exploring other ways to obtain the data, such as using an embedded table feature. We have discussed with UiPath that an embedded table feature would be beneficial.

Existing models have room for improvement. Sometimes, after we train a model, we still don't get the expected results.

The technical support has room for improvement.

I have been using UiPath Document Understanding for one year.

UiPath Document Understanding is stable but we have had some issues in the last few months.

We currently have a few hundred partners and would like to scale up to a few thousand, but the manual intervention required to use Document Understanding at our current results level would prevent us from scaling up until better training models are available to reduce the need for manual intervention.

Technical support does not always provide a proper solution to our problems. Instead of providing an actual solution to our current enterprise system, they suggest that we upgrade the solution or move to the cloud.

Neutral

In my previous organization, I used a tool called Conexiom. UiPath Document Understanding is easier to use and train the models with. We have people in our organization who are not trained and are still able to use Document Understanding.

The initial setup was straightforward and it was completed in one day. 

The price is on the high end.

I would rate UiPath Document Understanding seven out of ten.

We do not include handwritten PDFs in our process because we conducted a proof of concept and the results were not accurate. I believe this is because we did not use the required machine-learning model for handwritten PDFs.

We have a team of ten people who use UiPath Document Understanding.

Maintenance is required to validate the data.

I would recommend UiPath Document Understanding to anybody considering it. 

I use UiPath Document Understanding to extract data from digital invoices.

We implemented UiPath Document Understanding to eliminate the need for manual invoice data extraction.

We process 50 invoices per day for a logistics organization. We receive the invoices from the user, and a bot reads them one at a time, extracts the details and stores them in an Excel file. If the extraction does not match the confidence score, we send the invoice to the Action Center for manual validation. Once the data is extracted, the bot enters it into SAP and validates it against the system information.

We used machine learning to train a model to process the different formats we were receiving.

Seventy percent of the documents we process are completed automatically without any manual intervention.

The AI and machine learning capabilities of UiPath Document Understanding are good.

Once trained on a large dataset, AI can save us significant time by performing tasks that are beyond our capabilities.

Human validation is required for about 25 percent of the documents we process. For smaller documents, it takes a human a few minutes to validate the information.

Our average handle time after implementing UiPath Document Understanding is five minutes.

UiPath Document Understanding has helped reduce human error.

UiPath Document Understanding has helped free up our staff time.

We can see results as early as two months, and definitely by six months.

UiPath Document Understanding offers multiple types of extractors, which we can use to extract information from documents in a variety of ways. Machine learning is a particularly useful method, as it allows us to train custom models to meet our specific needs.

UiPath Document Understanding has difficulty identifying handwritten documents, and there is room for improvement.

Some of the invoices we process are stamped, and the AI has difficulty understanding them because the stamp format varies between a square and a circle. Resolving this issue will allow us to process more complex documents using UiPath Document Understanding.

I have been using UiPath Document Understanding for one year.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

I would rate UiPath Document Understanding eight out of ten.

The solution was deployed in a single department for multiple users.

Maintenance is required for continuous training whenever an invoice format changes or a new vendor is added.

I recommend conducting a POC with the community to ensure that UiPath Document Understanding meets the organization's requirements before fully implementing it.

I have been using it for processing invoices, receipts, and multiple forms of documents. We have structured and unstructured documents. Structured documents include forms, and unstructured documents include written documents and receipts. For identifying the type of document and for processing those documents, we use UiPath Document Understanding. It helps to identify the type, extract the information, and process the information.

We are able to process multiple documents and extract information from them. We can put that information into an Excel or a PowerPoint file. We can also put that on a website or in an application such as SAP. We can generate a PDF. We can do all this with UiPath Document Understanding. So far, I have only exported information into an Excel file. I have not integrated it with any other system.

UiPath Document Understanding can extract information from unstructured or handwritten documents. It can extract information from structured documents such as forms. It can process invoices. I have mostly tried it with structured documents, and I have not had any problems with it.

UiPath Document Understanding makes the work easy. It can classify a document and capture the information. It is able to process a document quicker than a human. Its AI and ML capabilities improve productivity and accuracy. If we have a lot of invoices of a certain pattern, we can feed that pattern. It can then process multiple documents of that pattern. This way machine learning helps us to easily process more documents.

We can now design or develop a process much faster. Previously, we had to do string manipulation to extract information from invoices, which required a lot of coding knowledge, but with UiPath Document Understanding, that is not required. It automatically identifies the type of document and extracts information. It saves time and improves accuracy. It is a low-code or no-code solution, and even someone who is not skilled in programming can design a process. From half an hour to one hour, process designing now takes 10 to 15 minutes. 

Human validation is required for certain business processes. The Present Validation Station activity enables you to review and confirm whether correct values are extracted or not.

UiPath Document Understanding reduces human errors. Humans can make mistakes while extracting information or while coding. To avoid all those problems, we are using UiPath Document Understanding. It helps us reduce human errors because no coding is involved. We are directly teaching the system how to identify a document. It automatically identifies a document and extracts information from it. Because the system does that by itself, we can reduce a lot of human error. There is a 40% to 50% reduction.

The machine learning (ML) extractor is valuable. It helps in extracting information from even unstructured documents. It can sometimes also extract information from a written document. Without much manual intervention, it is able to process the documents. This is a unique feature of UiPath Document Understanding.

There could be a feature similar to ChatGPT where when you ask about a document, you get an answer about what is there in that document. If possible, this kind of feature can be incorporated so that you do not need to open a file and take the value. It should directly detect that instead of opening and validating the document. Such a feature will speed up the process.

I have been using this solution for the past two or three months. I have been using it only for practicing. I am not using it for any industrial purpose.

UiPath is giving very good support. They also have some webinars, which are very helpful in developing our solution. I would rate their support a nine out of ten.

Positive

Before UiPath Document Understanding, I was using string manipulation and screen scraping methods for extracting values from invoices. It used to take more time to build a solution. 

It is deployed on-prem. Its deployment was straightforward. It took 10 to 15 minutes.

In terms of maintenance, bots require some maintenance. Bots can fail, so continued maintenance may be required.

I deployed it myself. I am using it on my own.

I have only been using its free version.

It is a very good solution. It improves productivity and accuracy. It reduces development time.

You can use UiPath Document Understanding even when you do not have programming knowledge. You just need to know the pattern of documents. It has ML capabilities, and the bot will automatically learn and start to identify and process documents. It is very easy.

Overall, I would rate UiPath Document Understanding a nine out of ten.

I use the tool for a couple of my client projects. My clients receive physical mail and may need to scan data to run processes like automation on it. Another use case is document classification. The solution helps with processes like classification, data extraction, and automation. 

UiPath removes manual processes and reduces human dependency. It takes a lot of effort to go through each physical mail or email, extract the data and transfer it to Excel. However, the solution automates the process and works 24/7. 

It gives a complete package to process and understand documents. The valuable features include taxonomy modification, classification, workstation, etc. There are out-of-the-box features like ML models which you can custom build.  

We have saved time with UiPath Document Understanding. We have seen a 50 percent improvement in scanning processes. Compared to humans, the tool runs 24/7. The human error rate has also been reduced. Our human error rate is five percent now compared to the previous 15 percent. 

UiPath Document Understanding has also freed up our staff who now spend more time on critical tasks. 

There is room for improvement in UiPath Document Understanding's pricing. It is expensive for small clients. Currently, there is a big gap between the basic package and the 200,000 packages. There is no package in the middle for small agencies. 

I have been working with the solution for more than five years. I started to work on the product when it was still under development. 

I have not encountered any performance issues. 

The exact number of documents processed per client varies. However, it ranges between 1000-3000 per week. The documents processed are very less. We process 10-15 documents daily. 

UiPath Document Understanding's support is always ready and helpful. 

Positive

UiPath Document Understanding was easy to implement and put into production. The timeline can change when you create your ML model. 

We have seen ROI with UiPath Document Understanding. 

The document format is mostly PDF and can be structured or semi-structured documents. We have not dealt with handwritten documents. Our real-time use case is for structured documents like emails and invoices. Most of the client documents go through without any errors. However, there is a five percent failure rate that needs to be considered since the document may contain unexpected data. 90 percent of documents go through it. 

The solution handles signature-based documents. We are still working on that prototype. We faced issues with seals. It differs from department to department and state to state.

The tool's AL and ML features work fine for us. We leverage these features for driving licenses. AL and ML keep a check on document generation. UiPath Document Understanding has come up with an API-based document understanding model which we will leverage soon. 

We implement human validation when we use anything new so that everything works as expected. 

I would rate UiPath Document Understanding an eight out of ten. 

Our use cases are in the banking sector. We have certain PDFs that contain customer data, and we have to extract data from them and compare it with the data provided earlier. The PDFs can be scanned, handwritten, or native PDFs.

It gives us a complete, unattended process for extracting details from PDFs. Otherwise, that would been redundant work done by specialists. It is saving us around 800 workers.

For one record, we used to take about half an hour, but Document Understanding completes things in seconds. It's very quick. It has also reduced human error a lot.

In UiPath Document Understanding, I use two extractors. One is the Intelligent Form Extractor and the other is the Machine Learning Extractor. The first one is very useful and user-friendly. When we have a single template or two to three templates, Intelligent Form Extractor is much easier to use. But if we have multiple templates, it's better to go with ML Extractor as you can train the model with different templates.

The artificial intelligence features are very good. The more you train it, the more you will benefit from it.

Also, integrating Document Understanding with other systems or applications is not complex. It's quite easy. Even for attended processes, if you combine UiPath Document Understanding with Action Center, it's almost like unattended processing.

Document Understanding is a combination of the extractor and OCR. Depending on the taxonomy that you give and the classification that you define, it is accurate. But there are multiple OCRs and I find UiPath Document Understanding to be as accurate as Microsoft or Textract OCR. If the accuracy was improved, it would be much better.

There are also problems with handwritten documents. The results from those are not 90 percent accurate. With scanned and native PDFs, it is 90 to 99 percent accurate, but the accuracy of handwritten documents is somewhat less. If they could improve on that, it would also be helpful.

Also, in the banking sector, we can't use the cloud-based Orchestrator or cloud endpoints for OCR. Getting approval for ML Extractor takes a lot of time for us. Instead, if we could go with Intelligent Form Extractor, it would save a lot of approval time. If they could improve and add features to Intelligent Form Extractor, it would make our lives easier.

I have been working with UiPath for five and a half years and with Document Understanding for one and a half years.

I have not experienced it crashing or lagging.

The scalability is good. I haven't seen any waste of money.

We have not contacted their technical support very much.

We do not use UiPath in the cloud; we have our own internal version, so the initial setup took time for us. But that had nothing to do with UiPath. It was because of our company's policies.

It took time for people to understand it, but once they got it, it went well.

It took some time because Document Understanding is pricey compared to other tools in UiPath. After 10 to 15 days, when there were fewer errors, that's when we started to see profits.

I would suggest combining other technologies with UiPath. That's when you see more benefit from Document Understanding.