V7 sped up our labeling 9–10x compared to VGG. The appeal of using V7 is that it's commercial off-the-shelf, very intuitive, and easy to use for non-technical people.Read case study ->
We use V7 to make our annotation and model training workflows more efficient. It's much easier to use than other software thanks to its intuitive UI.Read case study ->
From the operations perspective, the scale of the projects that we are working on required a computer vision tool like V7 to complete them.Read case study ->
Automate document processing end-to-end with V7's public text models. Build custom data pipelines and process documents 10x faster
Plug in V7's Text Scanner into your custom workflow and extract text data in minutes. Bring your own models and re-train them for higher accuracy.
Use V7's Invoice Scanner to extract invoice data, such as total amount or due date, and line-items such as product name or unit price. Generate labels and attributes for each field and automate invoice processing at 10x speed.
Use V7's Receipt Scanner to recognize and extract data from receipts. Detect key information, like store name or purchase date, and generate labels and attributes. Plug in your own models, feed them data, and train them iteratively.
Use V7's Passport Scanner to detect and extract data from passports, such as number, expiry date, or personal information. Add labels and attributes to get a clear view of all passport data. Build and automate custom workflows.
It fits our business needs best due to its customizable workflows, simple setup, consensus stages, and the ability to monitor annotator performance and project progress. It's also very intuitive and stable.
Input data into workflows via the API and watch your documents get populated with accurate data automatically
Multiple languages, multiple alphabets, even in the same document. Extract text with our built-in OCR models or bring your own.
Read in any scenario, whether its a pdf, tif, photo of a receipt, video, rotated image, or heavily skewed document.
Thousands of agents ready to help your business—label and refine documents every day to reach 99% accuracy.
Hire or bring your own labeling team to create training data rapidly or bolster human in the loop processes.
Combine multiple models in a workflow, solve tasks that bridge the gap between vision and NLP, or compare old models with new ones.
Send your documents to the right agents based on detected fields or document types to assure efficiency and accuracy.
Combine your document processing models with V7’s expert human reviewers to take your neural networks to superhuman levels
Solve any labeling task 10x faster, train accurate AI models, manage data, and hire pro labelers that care about your computer vision projects.
Send tasks to other ML-Ops platforms, host data privately in your enterprise cloud storage, and load datasets into your deep learning framework of choice. Unleash the potential of your project with a thriving ecosystem at your disposal.
Yes, IDP can have a positive impact on operations by streamlining document processing, reducing manual effort, and improving data accuracy and accessibility.
Yes, IDP can improve compliance by ensuring that all documents are processed accurately, consistently, and in accordance with relevant regulations.
Yes, IDP can reduce costs by automating manual document processing tasks, improving efficiency, and reducing errors.
Yes, IDP is different from document capture. Document capture refers to the process of digitizing documents and capturing the images, while IDP focuses on extracting and processing the data from those documents.
IDP supports various document formats, including PDF, Word, Excel, and scanned images.
IDP can recognize and process documents in multiple languages, but the accuracy of the recognition may depend on the complexity of the language and the quality of the document.
Yes, IDP can operate in the cloud, enabling organizations to process documents remotely and access the data from anywhere.
Yes, IDP is better than traditional OCR as it uses advanced AI and ML algorithms to understand the context of the document and extract relevant information.
Some common use cases for IDP include invoice processing, contract management, order processing, claims processing, and HR document management.
There are several types of document processing, including document capture, document classification, data extraction, and document management.
Yes, IDP can process handwritten text using Optical Character Recognition (OCR) technology and various other techniques such as Natural Language Processing (NLP).
Yes, IDP can extract data from unstructured content such as emails, invoices, contracts, and other types of documents using various AI and ML techniques.
Document Processing Automation refers to the use of technology to automate the processing of documents, including the extraction of data, classification of documents, and routing of information to appropriate recipients.
Intelligent Document Processing (IDP) is an advanced technology that uses Artificial Intelligence (AI) and Machine Learning (ML) to automate and streamline the processing of structured and unstructured data from various types of documents.
