Amazon Textract offers comprehensive data extraction capabilities, allowing for seamless integration with analytics and minimal document storage needs.
| Product | Mindshare (%) |
|---|---|
| Amazon Textract | 2.1% |
| ABBYY Vantage | 5.1% |
| UiPath IXP | 5.0% |
| Other | 87.8% |
| Type | Title | Date | |
|---|---|---|---|
| Category | Intelligent Document Processing (IDP) | Jul 31, 2026 | Download |
| Product | Reviews, tips, and advice from real users | Jul 31, 2026 | Download |
| Comparison | Amazon Textract vs Automation Anywhere | Jul 31, 2026 | Download |
| Comparison | Amazon Textract vs UiPath IXP | Jul 31, 2026 | Download |
| Comparison | Amazon Textract vs ABBYY Vantage | Jul 31, 2026 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| ABBYY Vantage | 4.0 | 5.1% | 93% | 51 interviewsAdd to research |
| UiPath IXP | 4.0 | 5.0% | 90% | 56 interviewsAdd to research |
Amazon Textract provides users with intuitive OCR functionalities and accurate data extraction, including key-value pairs with coordinates. It reduces manual labor through efficient API calls and improves processing of handwritten data. Users benefit from its ability to organize and extract data rapidly for spreadsheets, enhancing compliance and auditing efficiency. However, room for improvement exists with handling handwritten content and complex tabular data structures, as well as feature limitations like checkbox detection and offline functionality.
What features define Amazon Textract?Amazon Textract is primarily used in extracting data from bank statements, receipts, dental claims, EOB documents, and resumes, serving healthcare and recruitment sectors with precision. Organizations process claims and extract valuable data from both digital and handwritten documents, feeding this data into LLMs for further analytics. Integration with Bedrock enhances abilities in projects, providing structured data extraction for better document management.
| Author info | Rating | Review Summary |
|---|---|---|
| Solution Architect at Skillnetinc | 4.5 | I've found Amazon Textract, integrated with Bedrock, effective for automating resume processing, with great AWS support and scalability, though integration with other systems could improve; overall, it's stable, affordable, and easy to set up. |
| Deputy Manager at Deloitte | 4.5 | I've used Amazon Textract for years to digitize scanned and handwritten documents for ERP analytics. It's accurate, reduces manual work, and integrates well, though I wish it had an offline option for clients with poor internet connectivity. |
| Machine Learning Engineer at a tech services company with 1,001-5,000 employees | 3.5 | I used Amazon Textract to extract structured data from dental claim documents; it performs well with key-value pairs and OCR, though complex tables and checkboxes need improvement. Overall, it reduces manual work and is easy to use. |
| Software Engineer at Metatechno Lanka Company (Pvt.) Ltd. | 2.0 | I used Amazon Textract to read bank statements and receipts. It was straightforward and easy to use, but I had concerns about its accuracy, particularly with handwritten items and documents with pencil marks, where it often failed to deliver correct results. |
In an organization with an opening for a developer position, the organization receives hundreds of resumes. Instead of manually evaluating those resumes, Amazon Textract can be used to pull the content of the resume and send it to the LLM for processing to find out which candidate suits best for this position.
I have integrated Amazon Textract with Bedrock. In a project, I integrated Amazon Textract with Bedrock and passed the extracted document text to the LLM using Bedrock.
Bedrock is basically a framework that can manage multiple large language models. Another useful tool is Amazon Textract which extracts text from documents.
It helps with compliance because Amazon Textract itself doesn't store anything. When hundreds of documents are uploaded in an S3 bucket, the S3 bucket will store the documents, but Amazon Textract itself doesn't store anything. It pulls the contents of the documents and then passes them on to the next system, making it compliant.
It helps in a great way by reducing the load on LLM and reducing the cost.
Some easy integration with other systems could be improved.
I have been working on this for approximately one year.
I think it's stable and would give it a nine out of ten. Sometimes there are issues in the region itself. If something is deployed in US-East-1 and there is a problem there, everything in US-East-1 would fail. That is something that happens, but that is acceptable.
It is quickly scalable. However, if there is a mixed architecture, for example, if an application is hosted in Microsoft Azure but using Amazon Textract and AWS Bedrock, there is a compromise on the scalability aspect.
We see very technical people there and I had to contact AWS support maybe 20-30 times in the past three-four years. The support is actually very good. I've worked with Azure and Oracle Cloud Infrastructure, but compared to the others, AWS support is excellent.
Positive
I am currently working with DevSecOps tools. Regarding OWASP ZAP, it's good and being used worldwide. It's a very good product, but due to some constraints in our organization, we had to move to another product.
It's easy and straightforward. There is detailed documentation from Amazon and lots of YouTube videos available, so anybody can do it.
Amazon Textract is not expensive, it's cheap. The LLMs, particularly premium LLM models such as Amazon's own LLM models Nova, are a bit expensive compared to Perplexity.
Organizations typically build the CI/CD pipeline. The main application could be hosted anywhere - it could be hosted on a machine, EC2, or it could be containerized. Some organizations do manual deployment, while others do integrated CI/CD using AWS CodeBuild or GitHub Actions. Different modes are available depending on what the organization is following.
I know about JFrog but haven't used it and don't have hands-on experience with it.
Amazon Textract already has many features built into it. I don't have experience with similar tools in the market. Nothing else comes to my mind at this time.
On a scale of 1-10, I rate this solution a 9.
I have worked with Amazon Textract and AWS OCR solution, and my use case involves working on a project in India that requires scanning data from both digital scanned documents and handwritten ones, which I need to translate into digital format for use in my ERP system for analytics and multiple purposes. For that, I am using Amazon Textract, as well as other use cases such as Bedrock for chatbot applications.
I have been using Amazon Textract for the last five to six years, approximately.
When discussing Amazon Textract, the handwritten OCR quality is good. I'm working with multiple OCR solutions for handwritten data, and Amazon Textract is much better than other solutions.
Amazon Textract is superior because it has features that allow you to use analytics or intelligent automation with the OCR. It will automatically identify key and value pairs, so you can use the data easily. For instance, if a PDF mentions a name, Amazon Textract can identify the key and value, translating that data automatically.
Implementing Amazon Textract decreases manual effort in document processing tasks. For example, if any data entry is done by one employee, using the OCR will automatically decrease the man-hours required.
Amazon Textract helps in compliance and auditing efforts because if I have a 100-page report that needs summarizing, I first need to extract data from it and input it into Excel. This data copy-pasting can be a challenging task for a human, but OCR simplifies it, enabling calculations to be implemented much faster and more efficiently.
They should provide an offline solution because in many areas in India and outside, there are clients facing Internet issues. An offline solution must be implemented for OCR.
Feature-wise, Amazon Textract is adequate and there is no need for improvements in other features.
I have been using Amazon Textract for the last five to six years, approximately.
From a technical support viewpoint, I work for Deloitte India, and AWS is a partner of Deloitte. I haven't faced any issues, and I find that support is accessible with a good community and many open-source solutions available on Google.
In the competition, I am working with Azure Cloud OCR, in-house solutions in Python, and I have also worked with ABBYY FineReader, which provides OCR solutions for offline use. Sometimes, we don't have Internet access, and in those cases, we cannot use Amazon Textract or other cloud solutions such as Google Cloud or Microsoft Azure, so ABBYY FineReader is my go-to for offline OCR.
The initial setup for Amazon Textract is not straightforward and takes some time. We need to understand what OCR is first and follow certain processes to implement Amazon Textract, which includes setting up system configurations using CMD. It takes time but is manageable for someone skilled in AWS.
My advice for other users considering Amazon Textract is simple: if they can afford to implement AWS, they should choose it because it offers a much better solution compared to the competition.
It depends on the project basically. If you have a good client who is willing to pay those charges for Amazon Textract, then it is cost-efficient. However, not every client is willing to spend that much on OCR solutions, which can be problematic when we need to look for alternatives.
On a scale of one to ten, I rate Amazon Textract an 8.5.
There was a use case for the healthcare domain where we have to process dental claims and EOB documents. For that document, we have to extract the information in the structured tabular format as well as key-value pairs and checkboxes from the dental claims and the explanation of benefits document. This extracted information will be useful for further processing.
Whenever we pass this document to the OCR, it is helpful for extracting the information from the document. For example, in the dental claim document, we have the patient name, doctor's name, and what procedure was conducted on the patient by the doctors. So all this information we have to extract from the given document in the tabular format, or checkboxes, or the signatures from the document. Amazon Textract helps me to extract tabular forms, signatures, and key-value pairs separately.
Amazon Textract is good at extracting the key-value pairs and providing coordinates for the particular key-value or text which you are extracting from the document. It is very useful to get the coordinates, as we can map that coordinate again into the document and showcase where exactly the extracted information is located in the document.
I am mostly using the OCR feature of Amazon Textract.
With the help of Amazon Textract, we are reducing labor and manpower because with just one API call, we can get all the extracted information with its coordinates. Doing that by coding or using other open source OCR solutions is not as effective as using Amazon Textract, which helps reduce manpower.
Extracting the information with Amazon Textract is super easy. With one API call, we can extract the information, and the extracted information is correct along with the properly correct coordinates. I would recommend it to others for sure.
The main benefit of using Amazon Textract needs improvement, specifically when we extract the tabular data, it is very complicated to get its coordinate functions. We get the coordinates for child and its parent and its child, which is very difficult to extract for the tabular structure or CSV format. Sometimes the tabular data does not process properly for complex tabular structures or complex tables. It would be helpful if we could improvise the model or the OCR solution to extract the complex tabular data and get its coordinates or bounding boxes for the tables, especially for complex tables.
Apart from this tabular data, there is also no solution for checkbox detection. It would be helpful if we got a solution to detect the checkbox and get the bounding boxes for the checkbox and its value.
I have used Amazon Textract for almost two to three months. After that, I am also using Bedrock for a few of the service projects.
I have not integrated Amazon Textract with other AWS services such as Comprehend or Augmented AI.
We have integrated Amazon Textract with Bedrock. Whatever information we extract, we again pass it to the LLM call and we do some preprocessing and post-processing on that, getting useful information.
For savings, I cannot provide information because we directly chose the Amazon Textract solution. We mostly work with AWS services, so we directly use Amazon Textract without considering any other services from Google or Azure. Hence, I cannot comment on the cost.
I was not involved in the initial setup of Amazon Textract or the onboarding part.
Most of the setups are done by the DevOps or framework engineers, so I did not face any difficulty while utilizing Amazon Textract at the initial level.
Amazon Textract is perfect for auditing because I did not consider it from the perspective of auditing and compliance. I think it performs well in this aspect.
I did not think about the cost that much because we have to fulfill the customer's requirement. Whatever they demand, we do not think about cost at first. Whatever the cost involved, we will pay for that and use the services.
Our client requires the solution with the help of AWS premises or services only, that is why we always choose Amazon Textract.
I don't have any specific advice for now, but if I think of anything, I will surely share it. On a scale of 1-10, I rate Amazon Textract a 7 out of 10.
Negative
