UiPath Document Understanding Reviews

UiPath can handle normal, structured documents like forms and editable PDFs, but the data cannot be extracted from some unstructured documents with normal instructions. Non-standard documents are the most challenging thing for us. For example, let's say you have a hard copy of a receipt you get from a store, and you want to keep a record of it. You need to extract specific types of data and store it in Excel.  Document Understanding can deal with these documents. You can configure it to scan the receipt and identify the data we're interested in. 

We can provide a set of optimizations, classifications, and preconfigurations before we process the document. We created a taxonomy that we've predefined that these kinds of documents can conform to our security purposes. Using the taxonomy, Document Understanding can first classify the type of document, the arguments or variables we want to use, and the data we need to extract or store. Document Understanding can scan a written document and identify if a signature is present. 

We keep a person in the loop in between because we can't 100 percent rely on the extraction. Document Understanding uses OCR which sometimes struggles with handwritten material. For example, it might mistake a six for a five. There must be a human in the loop to ensure quality. The device will send it to the validation station on your mobile phone. The bot will learn from the choices you make, and it will be more accurate the next time.

Document Understanding helps us to reduce human error. It can reduce the time staff spends on some tasks, but the amount of time saved depends on a few factors. We still need to validate the data because before proceeding, we sometimes collect and share sensitive data for our clients. We need a validation step in between to check before we send any data. 

One benefit of Document Understanding is machine learning. As we process more data, we train Document Understanding to classify information more accurately. Document Understanding can extract and interpret information similar to the way a human can. A human can read a paragraph and distinguish between types of information, but our UiPath bots can't. Document Understanding integrates with artificial intelligence to interpret information within that. 

The newer versions of Document Understanding can integrate with ChatGPT or any generative AI tools so that it can better interpret the information autonomously, and we don't need to create the taxonomy or classify the documents. We only need to give a prompt and input the document. 

It will read documents similar to the way a human would. Let's use a contract as an example. You want to extract data like the buyer, seller, property address, etc. It will take the information from the document and give it to you. It can also scan for checkboxes and identify which ones are checked, but there are some limitations. 

It uses a document object model to map which data is on what page of the document. For example, let's say the data you are interested in is on the third page of the document. The model knows where the data is, so it directly jumps to that particular page and extracts the information. The mapping is very perfect. 

We always use attended processes because it's a good practice. The bot can do it without a human in the loop, but I would only do that if you are certain about which information you want to extract. If you're working with a handwritten document or signatures, you need a human in the loop to validate the data and help the machine learning component learn the difference between correct and incorrect information. 

The time required for the validation process varies depending on the number of fields. For a small number, it only takes two or three minutes. When you have more fields, it may take a little longer to create and configure the document understanding model. You need to create the taxonomy, classifications, and model.

The validation process is easy. The Validation Station shows you the extracted data on one side and the document on the other, so you can easily scroll down and check if the data is accurate. You just need to click a checkbox. If you don't think it is fine, you have the option to add an exception. Based on that exception, you can create multiple conditions for how to address the same issue if it happens again. 

Document Understanding is about 75-100 percent accurate depending on the type of document, and it increases as you train the model. 

I would like to see more integration of artificial intelligence. That's being implemented, but it would be a massive improvement to the solution's document processing. If UiPath achieves intelligent document processing, it will be far better than anything on the market. There are currently some limitations with the fields that could be addressed using a GPT engine. With an integrated AI model, you wouldn't need to create your taxonomy. You would only need to provide some prompts, such as "What is the property name?" It will store that as a variable.

I started using Document Understanding six months ago. 

In the community version, there is a limit on data extraction using a form-based extractor. There are limitations on digitization in the community version. You can do only 50 or so in one hour. The enterprise version can handle a larger volume of data, but we aren't dealing with huge amounts of data. We can still use multiple types. It allows you to scale with multiple types of extractors in the same document. If I'm confident in how the model is processing a particular field, it can be adopted into the regular business structure and reused. 

I was involved in the deployment only as a developer. We created the taxonomy and the model for Document Understanding, then tested multiple cases with multiple documents. We see which extractor would be the best fit for a particular value. We can classify it according to the values we want and we can set up an accuracy also. We can set a confidence level for each variable, so the confidence is different for a regular extractor versus a complex one. I set the confidence level high on the regular extractor. 

Initially, the deployment is somewhat complicated for a developer. However, it gets easier once you understand everything. We didn't need a consultant. I could complete the job by myself. It isn't rocket science. UiPath Academy has a free course on Document Understanding. Anyone can use it for free. 

We use the free community version. Anybody can use it, but it has some subtle limitations. The enterprise license gives you far better results without limitations.

Document Understanding can handle handwriting and signatures in most cases. The community version limits handwritten document processing, but it's enough for our needs and gives us the correct data every time. 

I haven't worked with any other document processing solution besides UiPath. I researched some tools, but Document Understanding seemed like the best fit for me, so I used it.

I rate UiPath eight out of 10. I deduct two points because creating the configurations can be time-consuming. 

Our old process involved manual data extraction from a large volume of documents with varying types and templates. This labor-intensive task required a significant workforce. We implemented UiPath Document Understanding to automate this process and eliminate the need for hand-coding solutions.

UiPath Document Understanding helps prepare data for machine learning by labeling documents used to train the models that will ultimately automate document processing tasks. We also use it to extract information from various identity documents like passports and ID cards, financial statements, credit card statements, and bank statements, and it can even process bank transaction data.

The documents we process using Document Understanding include tables and sometimes handwriting.

Around 70 percent of our documents are completely processed using Document Understanding.

The UiPath OCR works perfectly to extract handwriting, signatures, and multiple formats.

AI and machine learning prove valuable in training Document Understanding systems by analyzing data and identifying patterns, improving the system's ability to extract information from new documents.

AI streamlines Document Understanding by eliminating the need for manual coding. Users input documents into the AI, which then automatically classifies and extracts relevant information from each file. This saves staff over 20 hours a week.

UiPath Document Understanding integrates well with other systems.

For any newly implemented processes, human review will be necessary every day until Document Understanding is fully trained. The validation takes one minute per document.

The implementation of UiPath Document Understanding has saved us 50 percent of the time spent previously processing documents.

UiPath Document Understanding significantly reduces human error in processing documents, with complete accuracy achievable for standardized formats. However, its effectiveness in handling handwritten data varies depending on complexity.

UiPath Document Understanding helps save 20 percent of staff time to work on other tasks.                                          

The most valuable feature of UiPath Document Understanding is the AI Center.

UiPath Document Understanding, while effective for its own platform, could be even more valuable if it integrated with other commonly used platforms, allowing for a more universal approach to document processing.

I have been using UiPath Document Understanding for three years.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

The technical support is easy to access through the UiPath portal.

Positive

I've used IQ Bot from Automation Anywhere, Microsoft Intelligent Document Processing, and UiPath Document Understanding. IQ Bot and Document Understanding offer similar functionality, but only Microsoft's solution works across different platforms. We mainly use UiPath Document Understanding because it aligns with our client's preferred platform.

The deployment was straightforward. One person is enough for the deployment.

UiPath has a higher upfront cost, but its Document Understanding feature is not a significant additional expense compared to the overall platform.

I would rate UiPath Document Understanding nine out of ten.

We have six people that use UiPath Document Understanding.

I recommend UiPath Document Understanding to others.

We are a system integrator in the manufacturing industry and our clients use UiPath Document Understanding for their invoicing cycle processing.

Previously, our clients manually entered invoices into their systems, seeking a solution to automate this process while still maintaining controls for verification and audit purposes. We implemented UiPath Document Understanding to address this need.

Data entry is the most common use for UiPath Document Understanding.

In Italy, a common document format for simplified sales invoices is the BBT, which lists the total cost of the entire merchandise unit.

Since our clients are primarily small and medium-sized businesses, UiPath Document Understanding processes around 10,000 documents annually.

The documents contain a header and a large table where data is extracted.

Around 30 percent of the documents are fully completed by UiPath Document Understanding.

AI and machine learning do a great job sorting and identifying fields and documentation orientation. Managing different layouts is the most valuable attribute of AI.

Companies that use the UiPath platform can easily integrate UiPath Document Understanding using a few modules.

Human validation is required for 20 to 30 percent of cases and it takes less than one minute to complete.

UiPath Document Understanding helps reduce human error by 70 percent.

UiPath Document Understanding has helped free up around 70 percent of people's time to work on other projects.

For most of our clients, the time to value is usually six months.

The prebuilt algorithm for extracting foreign invoices is the most valuable feature because it eliminates the need for us to build anything from scratch.

The signature and handwriting are a pain point for the OCR and have room for improvement.

The extraction logic portion of the UI is not as user-friendly as the rest of the platform and has room for improvement.

I would like to have generative AI integration added to a future release.

I have been using UiPath Document Understanding for two years.

The stability of UiPath Document Understanding is good. The higher the stability the less maintenance is required.

The scalability can be improved with the help of generative AI. It is difficult to build an algorithm and move to another project without making important changes to it.

The technical support is good.

Positive

The initial deployment process is not user-friendly. It requires a lot of steps, although depending on the size of the deployment, one person can usually manage it.

The average deployment takes around one month to complete. 

We implement the solution for our clients.

The price for UiPath Document Understanding is a bit expensive.

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

The number of people required for maintenance depends on the project. It can go from one person up to seven.

UiPath Document Understanding is a key tool we use to automate document processing for our clients, including tasks like invoice and sales order processing. We can create multiple workflows for different clients and even use it internally. To handle even more complex documents, we've also built custom models for specific data extraction needs.

UiPath Document Understanding helps our clients streamline data entry by accurately and consistently extracting information from both paper and digital documents. This extracted data can then be seamlessly integrated into their existing ERP or finance systems, eliminating the need for manual data input.

Document Understanding automates the processing of our invoices and sales orders, which are our most common tasks due to their semi-structured format. These documents share a typical organization with common fields, though we also handle custom documents like certificates and licenses across various states.

Document Understanding helps us process thousands of documents each day.

Thousands of documents are processed completely by Document Understanding each month.

Machine learning is the core of Document Understanding, where trained models extract data from documents. For simple forms, basic tools suffice. But in most cases, Document Understanding's built-in machine learning tackles complex documents. Generative AI features are new and basic for now but hold promise for the future.

The human validation required for Document Understanding outputs depends on the use case. We aim to get above 80 percent without human intervention. For some use cases, we're well above 90 percent. In just one minute, the human validation process can be completed for the small percentage of tasks, typically between 10 and 20 percent, that necessitate it.

While average handle time varied greatly before automation ranging from eight to ten minutes or even longer, data entry for sales orders with hundreds of line items was especially slow, taking up to 30 minutes per order. Automating the process with API integration significantly reduced this time to just one to two minutes.

Document Understanding helps significantly reduce human error, especially in crucial tasks like sales order entry for manufacturing clients. Mistyped entries can lead to incorrect production, rework, and unhappy customers. While the error reduction varies, estimates range from 18-20 percent to potentially as high as 40 percent in some cases.

Document Understanding significantly reduces manual data entry, freeing up staff time. For instance, one client eliminated a data entry role entirely, allowing that employee to focus on higher-value tasks. This is a consistent benefit – whenever we implement Document Understanding, the staff previously responsible for data entry can be redeployed to different teams, roles, or more strategic work.

UiPath's Document Understanding significantly reduces the effort needed to train a machine-learning model for our documents. Their pre-built models and tools for customizing them minimize the need for manual tasks like creating bounding boxes and training on uncommon examples. This allows us to achieve high accuracy and certainty in data extraction with minimal human intervention.

The rising annual licensing cost of UiPath's Document Understanding product is a major turnoff for users. This constant price fluctuation incentivizes companies to switch to competing solutions, potentially hurting UiPath's market competitiveness.

The technical support has significant room for improvement.

I have been using UiPath Document Understanding for three and a half years.

The technical support is bad.

Negative

Document understanding projects deliver a significant return on investment in two ways. First, by automating data entry tasks, they free up customer service agents to focus on client interaction, improving service quality. Second, this automation can eliminate the need for offshore data entry teams, potentially bringing those jobs back onshore and saving tens of thousands on overall costs.

UiPath's pricing model is complex and based on AI units, which are consumed during model training and use. This makes it difficult to predict costs upfront, unlike a simpler pay-as-you-go model offered by Microsoft. With UiPath, you purchase a bundle of AI units, and even if you don't use them all, you're still charged for the entire bundle. This can be less cost-effective compared to Microsoft's approach where you only pay for what you use.

I would rate UiPath Document Understanding nine out of ten.

We use UiPath to automate invoice generation in our manufacturing process. One large project I worked on was for electricity bill payments. This project involved document processing, and I gained some experience with document processing and process mining. From there, we started using UiPath Document Understanding for the bulk of invoices we were receiving. We then had to put those invoices into the document processing model because they had a uniform structure, but there were also some handwritten notes and values in different places. So, we had to opt for document processing. Right now, we are developing a proof of concept for one of our government websites. This involves tender documents. We download and process the tender documents, extracting data such as the quotation, validity period, and other details, and putting it into a database.

We are processing documents in the hundreds using UiPath Document Understanding.

The standard document contains header information such as the company name, quoted value, quotation price, and expiration date. There are also tabular details regarding the items to be delivered. The tabular structure has headers, but checkboxes are not present in this particular use case. In addition to the header and tabular details, the document may also contain handwritten notes.

We have deployed UiPath Document Understanding on-premises but given the choice we always recommend the cloud because it includes more features.

UiPath Document Understanding eliminated the manual process of extracting data from 50 different websites each day.

Our customers' documents vary by website, but their structure is fairly uniform. As a result, we were able to process approximately 70-75 percent of the documents automatically with very good accuracy.

UiPath Document Understanding can identify and export signatures and handwriting from clear documents, using machine learning.

AI and machine learning feed the unprocessed raw data to some of the custom machine learning models. I have been working as a backend developer, so I have experience with machine learning as well. I tried with some of my own models, and it was clear that the customization of these models to our specific data requirements is very impressive.

UiPath Document Understanding's ability to integrate with all the systems and applications in our environment depends on the specific requirements of our use case. If it is generating a good return on investment, then I will consider using it for document processing. However, if my requirements can be met without using document processing, I will definitely choose to use simple OCR techniques instead. Traditional OCR engines can extract data from documents and place it into databases, where it can then be manipulated. However, this approach can be time-consuming and error-prone.

UiPath Document Understanding has helped our organization improve. It is especially useful when there is ambiguity in documents, which is a common real-life scenario. Inbuilt OCR engines are often unable to perform data inspection accurately in such cases. Whenever we have a large volume of documents to process and need to ensure high accuracy, UiPath Document Understanding is our first choice. One of the key benefits of UiPath Document Understanding is that it provides a dedicated model for document processing. This means that developers do not need to worry about other details and can focus solely on the task at hand. Additionally, UiPath Document Understanding integrates seamlessly with machine learning and AI models, which further enhances its capabilities.

Some of our customers were reluctant to switch over, and for a long time, they did everything manually, so their documentation was very outdated. As a result, we were required to manually validate 30 percent of the documents. The time to manually validate depends on each document. If two or three fields are mismatched, it does not take much time to correct them. However, if the entire document is showing errors, that will add time to the manual validation process.

It reduces the risk of human error and the time we spend processing documentation, freeing up our staff to work on other projects.

OCR technology is undoubtedly the most valuable feature and the feasibility of integrating data processes with AI and machine learning models is fascinating.

The identification and extraction of signatures is the most difficult part of the process, and there is room for improvement.

The machine learning model needs improvement, as we receive more and more unstructured documents from clients that require a lot of manual validation.

I have been using UiPath Document Understanding for three years.

UiPath Document Understanding is stable.

UiPath Document Understanding is scalable.

I've seen many clients refuse to purchase the licensing when they see the pricing. They're quite impressed with the results, as the bot does so much work in less time with accuracy. However, when it comes to pricing, I've seen clients refuse to spend that much on the licensing cost for UiPath Document Understanding.

On a scale of one to ten with ten being the most expensive, I rate UiPath Document Understanding an eight on cost.

I would rate UiPath Document Understanding eight out of ten.

I definitely recommend UiPath Document Understanding to anyone who is trying to do any kind of document automation. In fact, I have some friends who are working on an RPA project using UiPath, and we have been discussing it. I recommended Document Understanding when it first came out, and I think they have been using it for the project.

I use UiPath Document Understanding to extract data from scanned images using OCR technology. For example, when we have invoices, we can extract data from them by creating a model for that particular template using OCR technology, artificial intelligence, and machine learning. Every invoice has its own template, so we can create a template model and implement it in UiPath to run a bot for the data extraction process. After extracting the data, we can store it in an Excel file or database, whichever we prefer.

We deploy UiPath Document Understanding in the cloud and then integrate it with our on-premises architecture using a single key.

UiPath can automate any repetitive task, such as data entry, data extraction, file downloading, and file uploading, in any financial services, banking, or health insurance sector. The document formats include tables and checkboxes.

It can extract handwriting and signatures as long as they are legible.

Machine learning capabilities can be used to retrain prebuilt models for use with other templates.

It has helped improve our organization by reducing human tasks and errors.

Whenever data is extracted from a document using UiPath Document Understanding, we receive a confidence level rating. If the confidence level is low, we send the extracted information to the Action Center for human validation.

UiPath Document Understanding does the work of three full-time employees.

Using UiPath Document Understanding for documents without business or application exceptions reduces human error by 100 percent.

UiPath Document Understanding's image file extraction feature is the best in any OCR solution.

The signature comparison feature of UiPath Document Understanding could be improved.

To my understanding, we can only integrate UiPath Document Understanding with UiPath. I would like the ability to integrate with other solutions.

I have been using UiPath Document Understanding for two years.

UiPath Document Understanding is stable. 

We previously used an Excel automation tool but switched to UiPath Document Understanding because it is a better solution for repetitive tasks.

The initial setup is straightforward. The deployment was completed by two people including myself.

The implementation was completed in-house.

We received a 60-day free trial before having to purchase a license to continue using UiPath Document Understanding.

I would rate UiPath Document Understanding nine out of ten.

Data extraction accuracy depends on the document's quality and format. The maximum percentage of accurate data we can extract using UiPath Document Understanding is 90 percent.

We started to see the value right after implementing UiPath Document Understanding.

We are a service-based company, having a team size of 120 RPA developers.

The core technology of UiPath Document Understanding is OCR, which can be either ABBYY or Google Vision. The drag-and-drop facility, decision-making accuracy, and user-friendly actions make UiPath Document Understanding helpful for users.

UiPath Document Understanding offers a feature that allows users to process specific documents, such as purchase orders or invoices. The tool provides the ability to define and segregate actions and highlight fields using something similar to rubber band techniques. This means, in an invoice, users can easily highlight specific fields within the document and train the model accordingly. The process is user-friendly and can be easily executed even by those with basic knowledge of flow.

One area of improvement for UiPath Document Understanding is the accuracy of handwritten documents. While the solution supports handwritten documents, the accuracy percentage is not as high as desired. Therefore, it would be preferable if UiPath could focus on improving the accuracy of handwritten documents in their next release. Although they currently support handwritten documents, the accuracy is still low.

Also, the documents for the solution are lengthy, and because of this, I say that the solution should incorporate some video sessions.

I have experience with UiPath Document Understanding, in which the users are provided with multiple types of Document Understanding and a process-wise range of available documents. Overall, I have been using the solution for three to five years. Also, I am using the latest version.

The solution is quite stable, and I am happy with its performance in my programs and processes. I would rate the solution's stability an eight out of ten

The solution's scalability with the cloud version is really good. I rate the solution's scalability an eight out of ten.

Around 30-40 people work on UiPath Document Understanding in our company. The extent to which we use the solution, particularly in terms of utilization of bots or document processing, is dependent upon the volume of usage. In other words, if there is an increase in the number of customers, we will likely increase the usage of the solution.

UiPath's technical support is good. Even in emails, they are responsive and helpful. They also have a very good library of troubleshooting documents and other resources. Overall, I am happy with the solution's technical support.

I have experience with Automation Anywhere. Also, UiPath Process Mining is a pretty good tool since they have documents which provide its users with a clear understanding. On top of that, they also provide videos. So it's quite easy for an end-user to understand.

Although the solution's initial setup is not overly complex, UiPath could work to make it more understandable for a layperson. They could improve the setup process by creating a more structured flow.

The time needed for deployment varies, depending on factors like the use of cloud services. With a Plug and Play model, UiPath provides cloud services and a link, after which we can begin using the solution. However, deploying on a private cloud may take longer, up to three to five days or even two weeks, depending on the hardware readiness.

In terms of the maintenance of the solution, let's consider a scenario where we need to initiate a programming project to develop a bot that can help in invoice transactions. Typically, this process can take anywhere from three to five weeks and requires at least one project manager who oversees a team member who understands UiPath and business processes. To successfully develop the bot, we need to first analyze and understand the relevant business processes. Once we have derived the business processes, we need to document the solution, including creating a PDD. This document outlines the process and how it will appear once automated. Apart from that, you should have a good set of developers who understand UiPath.

In our organization, we implemented the solution with the help of integrators.

The ROI from using UiPath Document Understanding is really good, and we currently see a return on investment of around 40% to 50%.

For the licensing part, in our company, we initially started off with a monthly plan, but yearly plans are also there. Cost-wise, UiPath  Document Understanding is good since it provides flexibility in its pricing. So, smaller firms, especially MSMEs, can opt for monthly or yearly plans based on their budget. As an end user, I wish they reduce the price of their pro version to 300 dollars since UiPath Pro starts somewhere around 400 dollars per month. UiPath's competitor, Automation Anywhere, offers a plan for 99 dollars per month.

I can definitely recommend the solution to other users. Overall, I rate this product somewhere around eight out of ten.

We use UiPath to process HR documents. It helps us with the last mile of the paperwork. We're dealing with a lot of offline legal documents that need to be converted to Word or PDF. We use UiPath to read the documents and process them into a digital form the company can use.

We're consultants, so we have to provide invoices for our services. We have a specific layout that's internally defined, and we store the information in SAP. It's processing thousands of documents. Most of them are PDF forms. Our customers and partners usually save these documents and send them to us by mail or email. Almost all of our documents are processed through UiPath.  

UiPath has saved us a lot of time because 90 percent of our document processing is now automated. I can't give an estimate of how much we have reduced human error or the number of resources needed, but I can say that we are using time more efficiently. The solution makes work easier for our staff and frees up time. When we automate most of the documents, staff has more time to focus on issues in the remaining 10 percent. 

I like UiPath's machine-learning capabilities. My first impressions were good, but we only use it in a few processes. We intend to expand our use of these features in other situations, but we're still learning how to use the AI functions. We use the OCR much more. 

You can use machine learning with OCR to read and interpret documents. You don't need to map out specific fields because machine learning predicts where in the form the data is expected. Machine learning can identify various types of data in different sectors. UiPath is easy to integrate. We have only integrated the solution with two systems so far, but it's working well. 

Sometimes we have challenges when we need to read something like a barcode, so we must use the Cisco algorithm to solve the issue, or I have to ask developers for help specifically to capture this kind of information. There are other processes, such as refunds, that we still must do manually because there is no way to use automation to solve this issue. 

We have used UiPath Document Understanding for nearly two years.

I rate UiPath's support an eight out of ten. When we call their support team, they always solve our problems. That's what matters. 

Positive

I rate UiPath Document Understanding an eight out of ten. It's an excellent product that's stable and feature-rich. We also have the option to augment it with some external features.