Capturing data from invoices and storing it in an efficient way defines a business in many aspects. Human error has the greatest percentage and hence inclination towards automation is the scalable solution. Adding to that, manual data capture consumes a lot of time.
In this blog, we focus on invoice data capture using fast driven technologies like OCR and Artificial Intelligence and help businesses to find faster ways to capture data from invoices and reduce manual efforts. By the end of the article you’d be able to figure out the better algorithm to process invoice data.
So, let’s jump right into it:-
Invoice data capture is the vital means of the payment process. It captures data from supplier invoices with automated means and stores the extracted information such as invoice number, supplier name, address, amount etc. Invoice processing also includes comparing the PO number with the invoice number, following up with any errors in transaction or transfers. The advantage of an automated invoice capture system is that it handles errors and saves time which helps to save delays and relationships between clients or vendors. However, it comes with several challenges. Let’s have a look at them in next section:-
There are several challenges to invoice data capture which needs to be acknowledged. These challenges can lead to inaccuracy while capturing the data from invoices.
Invoices can be tackled or sent in various formats or templates. It can be a hard copy, sent via email, through fax. Now-a-days small scale businesses prefer emails or messaging apps as a medium to send the invoice. This often changes the format to the invoice. Not only that, the same company may also have different invoice templates which leads to multiple invoice templates for the services to use. This variation may lead to complexity in Invoice data capture.
The poor quality may arise due to torn paper, blur image, uneasy background color, etc. This may lead to errors and delay in processing the documents.
Key-values can be identified with position information reference, but at times, there are no key identifiers for values, such as zip code, Invoice number, PO number, etc. This may lead to incorrect/invalid information and may need a manual check. If the Invoice number is predicted wrong, it may lead to errors in payments.
Table extraction is another challenge for OCR-based solutions. OCR finds it difficult to extract, classify, and arrange line items after extraction. Intelligent Document Solutions that can extract contextual information are preferred to accurately capture and identify invoice table data.
Hard-copy based Invoice management becomes tedious and stressful when it flows department to department. Thus, scaling the process becomes difficult and sometimes impossible.
Too many soft copies such as email, invoices need physical storage which makes it difficult to manage.
There are mostly two ways to automatically process invoices. The types of invoice data capture are discussed below:-
These solutions use template-based OCR solutions which require training for each invoice template. It is not adaptable to slight changes in layout. It is responsible to capture characters, numbers, and symbols from different layouts of files. The documents can be in any format or extension (Like - jpeg,png,pdf,docs,etc.). This method is the survival of today’s practice and termed better than manual crawling.
These solutions use AI & ML to adapt different invoice templates. Once trained, they can adapt to different templates and any layout changes. Document AI solutions such as Docsumo are revolutionizing the methodology of Data extraction and scanning. This tool uses cutting edge technology for optimal results.
To understand how automated invoice data capture works, let’s have a look at the video discussing how Docsumo helps you in data scanning, key matching, and data capturing from invoices:-
Invoice scanning automatically detects, scans and crawls information from invoices which is received by suppliers and vendors. It captures information by recognizing keys and matching it with valid data and can handle structure and unstructured data.
The main deal to use Invoice data capture is to ease business demands and solutions. The several aspect which can make a business grow by invoice automation is as follows:-
Now, when we are trying to answer a question on what a good scanner does, we are trying to find an answer which relates to scalability and optimal extraction of data. A good algorithm is capable of dealing with any format (JSON, PDF, CSV, XML) and extracting key information.
Another thing which a good invoice scanner must have are the features of the automated algorithm. Invoice scanning may occur to known templates and unknown templates of Invoices.
Known format for invoices deals with the fixed set of invoices for the companies vendors and suppliers which is processed in the identical format. In such cases the algorithm can be pre-trained and used over and over again for the bunch of new invoices for the same vendor or supplier. We can anytime refine or rebuild the pre-trained model according to our convenience.
Unknown format of invoices deals with different sets of formats with changing suppliers or vendors. In this case different invoices need to be captured and stored. Businesses can leverage technologies like AI/ML to work on the solution of handling different kinds of invoices.
Additional required features for a good invoice scanner are:-
Because we have all the above-mentioned criteria covered. Docsumo makes the data capture much faster and smoother from invoices without compromising on accuracy. Docsumo comes with a pre-trained invoice data capture API is 99%+ accurate when it comes to field level data extraction.
Schedule a free demo today to to streamline your accounts payable automation end-to-end.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.