Want to learn how you can upload company’s invoice data directly to your downstream application (such as CMS, CRM, etc.). Well, we have a solution for you.
Your business or organization purchases goods and services from multiple vendors. The invoices they send you are processed by your accounting department. But the problem is that most invoices and receipts are meant to be read by people and not by software systems. So accounting teams have to enter the data into your accounting system, check it for problems, confirm the details with other departments, and release payments as per each vendor's payment terms. In some cases, specific data points are pulled to be stored for business or marketing purposes.
If you're a medium or large business, you may receive hundreds of such invoices or receipts every day. Processing them is a process that requires a ton of manual hours and resources to ensure the data is stored correctly in software such as Quickbooks or your internal CRM.
But this process can be transformed into a faster, automated, and less erroneous process using automated invoice data extraction systems. They use machine learning to extract the different data points and understand what they are (price, name, quantity, product) in any invoice — even a paper or handwritten invoice — regardless of its layout, formatting, language, currency, and other details. They extract all that information as structured data that can be consumed by downstream systems such as ERPs, CRM, and internal databases.
To appreciate their benefits, you need to first understand all the typical problems businesses face when processing invoices using other methods.
VisionERA’s automated invoice extraction capabilities offer state of the art accuracy in a fully automated and hands off pipeline that processes your invoices in seconds. This IDP platform can be customized to accept invoices from your internal workflows and automatically push the grabbed invoice data to your CRM, ERP, DB, or email. The pipeline is fully customizable and regardless of whether you have zero invoices or millions you can deploy this architecture and start processing documents in just 6 easy steps.
The first step is planning how to include our system in your existing business practices. This involves answering questions like:
The answers to such questions help us assess your integration and deployment requirements. For example, if you want thousands of historical invoices processed in a short time, we deploy additional cloud infrastructure to process your workload. If you're a large enterprise with hundreds of general ledger codes, then we train a secondary machine learning model to automatically generate a code for each invoice line item.
Our system supports a wide variety of invoice file formats and sources. It does not require you to provide invoice templates or define a given set. In this step, you should set up your invoice input pipeline and tell our system about the sources of invoices.
If some of your vendors still send paper invoices, we should set up a data capture pipeline for invoice scanning and digitize them to image formats like PDF. A scanner is perfect if you have it, but our system is capable of processing images/PDFs with varying levels of quality.
The second approach is probably the easiest way to integrate our automated system into your accounting workflows. Equip your current manual processors with photo taking tools and set them up to transfer the invoice photos to internal network storage or to cloud storage like AWS S3. Our system can automatically fetch new files from there and process them.
Many businesses already send PDF invoices or other digital formats. Our system can read these files directly and process them without any invoice templates. We’ll configure our system to read them from your internal databases, emails, or others.
Configure Integration With E-Invoicing and Accounting Systems
If your business has an invoice management system (like FreshBooks, QuickBooks, Zoho Books, Xero, or Pilot) or an accounting system (like SAP FI), our system knows how to query and fetch invoices directly from them using their APIs. Our system does not need fine-tuning to start processing your invoices from the most common invoice management systems.
Fine-tuning the deep learning models used to extract text and understand the relationship between text and fields allows us to go from the baseline models built to support as much data variance as possible to a domain specific pipeline optimized for your use case. Fine-tuning also allows for us to add unique fields or special remarks to the list of fields that are extracted when your invoices are processed. This is a huge part of the equation as it allows us to take the state of the art architecture we’ve built and fully customize it to your business workflow.
Right out of the box, our system understands invoice layouts produced by popular invoicing software like FreshBooks and others at a high level with good data variance coverage. We want to boost the accuracy for your specific business workflow and will fine-tune the deep learning models on real invoice and receipt examples from your business.
Layout variations may not be your only concern. While our model supports over 50 of the most common fields right out of the box, you may want to extract additional important information according to your unique accounting practices. For example:
If you're a government agency, you may require your vendors to follow some rules for their invoice numbers.
If your suppliers are offering payment terms that are different from the usual "net 30" (i.e., full payment within 30 days), your accounting department would want to know about them to avoid late payment penalties.
Our customizable pipeline allows you to add new fields that you want to extract on top of the 20+ default fields provided. We handle all the fine-tuning required to add these new fields to your specific pipeline.
Customizations based on one vendor's invoice can be automatically applied to all invoices across any vendor, or a subset of invoices. Our system then automatically looks for semantically similar information in every invoice (including older invoices if you want) to populate the relevant fields.
Some of the custom information we have extracted or added using such techniques include:
The ability to integrate with popular accounting systems and practices straight out of the box is an important requirement for any automated invoice data extraction that aims to save time and effort. Our system comes with the following built-in export and storage integration features:
Export the invoice data in multiple formats — PDF, Electronic Data Interchange, Excel, JSON, Comma-separated Values (CSV), and many more.
If you're a large enterprise, send the grabbed invoice data to your accounting system like SAP FICO. If you're a small business, export the extracted data to an accounting service like FreshBooks using their APIs.
Once you have configured and fine-tuned our system to your invoices, it's ready to process your invoices in bulk and start extracting structured data with minimal manual intervention. Let's understand how it works under the hood.
First, let's understand the high-level end-to-end processing of an invoice.
The heart of the system is a state of the art deep learning architecture. We’ve trained it on thousands of invoices to help it detect text, recognize the text characters, and associate invoice elements with appropriate fields. To do this, the system uses various characteristics of these elements, like their:
In the third step where you refine it by providing your invoices, we clone our latest model and fine-tune it on your invoices to create a model that's adapted to your invoices. Your preferences, like custom fields, help further refine this model to identify the data you want.
This customized model is now ready to process your new invoices. It scans each invoice to extract visual and linguistic characteristics. The combinations of characteristics trigger different areas of the neural network to identify an element as an address, purchase order, invoice number, etc.
Some elements may require additional processing. For example, a secondary deep learning model classifies the line items identified in your invoices to output their general ledger codes.
The primary output of this phase is a set of fields and their values. This data is then routed to one or more export pipelines to produce reports in different formats or export data to an accounting system.
Text detection and text recognition are important steps in this process. Text detection classifies regions in the input invoice as text or non-text elements (like lines of tables). Our system can identify handwritten text, irregularly oriented text, and signatures as text.
Text recognition involves recognizing the characters in the text. This is done based on visual features and a language model that gives the most probable words given the neighboring characters and the typical words in invoices. Such text understanding using multiple features provides far higher accuracy than plain OCR.
We use a state of the art deep learning pipeline to handle extracting visual features, text, and any other fine-tuned fields. This is used to get all the text from the invoice as well as any visual information that will help us understand what fields in your system match to what data.
With all text fragments accurately identified and located, the final subnetwork is a deep learning model that identifies those fragments as fields or field values. It does so based on their meanings, positions on the page, and positions relative to each other — exactly like a person would. For example, a string that has numbers and dashes in the upper regions of an invoice is probably the date of issue while a date in the lower regions is probably the last date of payment. Using such knowledge, it identifies a text fragment as a field value and classifies a probable field name for it.
The final outputs of this model are a set of detected field values and their field names.
While we aim for minimal manual intervention in bulk invoice processing, monitoring metrics is necessary to ensure accuracy, and real-time alerts are needed to inform personnel about the progress or any issues that come up. Our system also tells you confidence scores for each processed invoice. Invoices that are proving problematic for the model show low confidence scores. This is a great way for you to monitor the results of the invoice processing at a high level without needing to evaluate each invoice.
You’ve seen our architecture used to extract data from invoices using artificial intelligence and deep learning to provide an instant reduction of manual effort and costly resource consumption. The benefits are instant as you start to extract structured data and process it into your accounting system with state of the art accuracy and zero human input required.
Note: VisionERA is currently being offered at $0 invoicing.
At VisionERA , we have years of expertise in developing highly accurate information extraction systems using the latest deep learning innovations. We can customize our invoice processing solution to your exact business requirements and use cases. Contact us to see a demo of our invoice processing! You can also try-it-out using our trial version.