Invoice Processing with OCR
OCR in a Nutshell
In short, Optical Character Recognition (OCR) is a technology that allows the conversion of text found in scanned documents into machine-readable text. Scanned documents may contain printed text, text elements in an image, or handwritten text.
OCR incorporates various techniques in order to improve the accuracy and speed of the extraction process. It often goes hand in hand with some of the most advanced AI-based technologies to overcome the limitations that come with traditional OCR.
Optical Character Recognition is an invaluable tool for businesses who seek to digitize loads of physical documents and retrieve key elements from them.
Invoice Processing with OCR
Among the many applications of OCR capabilities is the ability to process and extract data from scanned financial documents, notably bills and invoices. Invoices typically incorporate several defined fields such as invoice number, issue date, due date, and due amount, among others. It is particularly important that each captured portion of data is assigned to its corresponding field to make it searchable and manageable.
Once digitized, the invoice data is usually integrated into other management systems to make the financial aspect of businesses more organized. It shouldn’t be too hard to imagine to what extent this can be helpful, as opposed to manually reviewing physical invoices.
Challenges of OCR Invoicing?
OCR is rarely fully automated. Just like most OCR applications, extracting invoice data processing using OCR does come with a few limitations. That explains why OCR is usually enhanced by other advanced technology to maximize the results and improve the accuracy as much as possible. Below are some of the challenges that come with OCR invoicing.
We mentioned how invoices contain various fields from which data should be captured. The styles and templates of these invoices may differ, which makes it quite difficult to define the correct labels automatically. For instance, if you come across a new invoice format with new labels, the difficulty lies in figuring out whether a data excerpt is a field or a value.
This requires familiarity with the new format and, often, human intervention to manually define the new labels. Otherwise, the OCR process can be prone to error.
When an image quality is sub-standard, it may be difficult for humans to recognize characters and values, let alone OCR. This is especially challenging when it comes to old, hardly legible documents. Even with the OCR pre-processing and image enhancement features, accuracy may not be guaranteed.
To overcome this limitation, an adequate image quality should be provided. This can be achieved with an effective image enhancement or by use of AI-based technologies to correlate the extracted data with data from other documents. Note that the latter option can be useful for fields not numeric values.
Businesses that resort to OCR to digitize their invoices may be dealing with many handwritten invoices. A powerful OCR tool can do a great job extracting handwritten text, especially with the help of AI. These systems are trained with loads of handwritten documents to better detect the many different handwritings. However, accuracy is rarely achieved in this case. While OCR can accurately detect good-quality printed documents, human intervention may be necessary to double check the recognition of handwritten invoices.
How Does OCR Invoicing Work???
We’ve seen how a low image quality can compromise the accuracy of invoice data extraction. To reduce the risk of error, a pre-processing stage is crucial.
This phase aims to enhance the scanned image to make its various fields and values more detectable. For this purpose, various techniques come into play here. Layout analysis, binarization, de-skewing, and many other techniques can significantly improve the accuracy of the OCR output. Pre-processing can also be assisted by AI-based technologies, notably machine learning and deep learning.
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Once the image is effectively pre-processed, the important process of segmentation takes place. The text data is segmented on various levels: lines, words, and, in case of handwritten text, characters. This is done by horizontal and vertical projections of the scanned image, followed by the recognition of white spacing between words or lines.
Once the data is properly segmented, the most essential stage of OCR invoicing involves the extraction and retrieval of key information. This involves the correct detection of invoice elements and placing them in their respective fields. To achieve this, many advanced techniques such as Named Entity Recognition (NER) and document categorization methods are used. These are intricately linked with AI-based technologies such as machine learning, NLP, and deep learning.
To ensure an accurate and fully automated data extraction, the OCR tool may take advantage of pre-trained models, as this will provide a reference on which the OCR tool can fall back on.
After the extraction and detection of invoice information, the accuracy of the whole process may further be improved. Spelling and grammar errors, for instance, should be dealt with. This stage usually takes in the various AI capabilities to make the necessary changes. Dictionary-based, rule-based, and machine-learning based techniques are employed in order to automate the mistake correction and the limit or eliminate the need for human intervention
In the end, the efficiency of OCR invoice processing comes down to the ability to incorporate all the necessary techniques to reach an accurate output. On its own, OCR can be significantly limited. However, when assisted with AI-based methods, it is a remarkable solution that can spare you a huge amount of time, money, and effort.
OCR Invoicing with AI
In recent times, automation has been at the very heart of modern industry needs. With most businesses seeking to achieve an effective digital transformation, OCR invoice processing is a cost-effective solution for the time-consuming chore of going through countless physical invoices and integrating them into computer software.
Automation immediately refers to the vast field of artificial intelligence and its various subfield. Indeed, OCR invoicing goes hand in hand with advanced technologies such as machine learning, which result in accurate, and automated OCR systems. This powerful combination significantly reduces, and even eliminates, the human effort required for various time-consuming tasks associated with traditional OCR methods.
Merging AI-based technologies with OCR invoicing has proved very useful inasmuch as it automated a wide range of repetitive tasks and made the whole process a lot easier, faster, and more effective.
Benefits of OCR Invoicing
Now that we’ve seen how OCR invoice processing works, it stands to reason that businesses can greatly benefit from this in many ways. For instance, OCR:
Invoice Processing with UBIAI
Thanks to the various features that come with UBIAI’s software, your scanned invoices can be processed in a quick and effective manner. The tool allows you to upload invoices in multiple formats. The various invoice labels, as well as their corresponding values, are then identified. In the process, the various stages of OCR that we mentioned take place to maximize the accuracy of your output.
Additionally, UBIAI’s tool makes use of the world’s most cutting-edge technologies to automate the invoice recognition and reduce the need for human intervention. The key advantage of using UBIAI’s tool is its sheer flexibility, taking into account your specific invoice templates. It allows you to pre-define your labels and, even more significantly, train and deploy your own models to achieve an accurate and automated invoice processing system that suit your particular business needs.
UBIAI’s practical and easy-to-use interface supports over 20 languages and offers a convenient solution for all kinds of businesses to deal with their physical invoices, among many other types of documents.
Bottom Line
Leveraging the power of OCR systems is essential to many modern businesses seeking to transform heaps of physical data and integrate them into digital systems. The convenience of AI-enhanced OCR is increasingly appreciated by various businesses, due to its enormous cost and time-saving potential.
Undoubtedly, OCR invoicing is a huge upgrade on the traditional system of manual classification of tons of physical invoices. With more breakthroughs in AI and its various subfields, invoice processing using OCR is more accurate and efficient than ever before.