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In this article, you're going to learn how to easily integrate an OCR Invoice engine in your project and how to choose and access the right engine according to your needs.
Optical Character Recognition traces its roots back to telegraphy. On the eve of the First World War, physicist Emanuel Goldberg invented a machine that could read characters and convert them into telegraph code. In the 1920s, he went a step further and created the first electronic document retrieval system.
Early versions of OCR had to be trained with images of each character and were limited to recognizing one font at a time. In the 1970s, inventor Ray Kurzweil commercialized “omni-font OCR”, which could process text printed in almost any font.
OCR Technology became popular in the early 1990s while attempting to digitize historic newspapers. In the early 2000s, OCR became available online as a cloud-based service, accessible via desktop and mobile applications.
Today, there’s a host of OCR service providers offering technology (often accessible via APIs) capable of recognizing most characters and fonts to a high level of accuracy.
Just like OCR for Receipt and Resume, Invoice OCR is a tool powered by OCR to extract and digitalize meaningful data from scanned or PDF invoices. Fields commonly captured by Invoice OCR include description, quantity, due date, line items, invoice number, merchant information, customer information, unit price, bill, receipt number, total amount, tax amount, etc.
This technology is built on multiple steps:
1. The first step in Invoice OCR technology consists in preprocessing the image —usually the scanned invoices are noisy so a preprocessing with noise removal and grayscaling are needed. This step is necessary for the text extraction engines to work well. Next step is text detection with OCR (for Optical Character Recognition). It extracts a text from various file types: pdf, Docx, JPEG, PNG, etc. Their goal is only to get the texts in the document without dealing with the structure of the document.
2. What follows consists of data extraction and categorization, where it classifies the extracted text into keys and tags like tax and total amount. It is based on deep learning algorithms and NER (Named Entity Recognition). The final result of the parsing is a structured form that can be readable by the computer. It’s often a JSON, XML or even a CSV file which makes it easy to be stored into a database and automatically analyzed.
Mindee helps software product teams build lightning-fast, accurate, and robust document processing automation features in their applications. Their API gives developers access to state-of-the-art deep learning algorithms for document parsing through an easy-to-use and developer-centric platform.
The full extraction process is performed without any humans in the loop, allowing you to offer real-time experience with a maximum level of data privacy. Mindee’s algorithms don’t need to read all the document text in its language to extract the relevant information.
Dataleon provides the best Machine Learning tools for data automation and processing. Ready-to-use API for data recognition and extraction are available to accelerate digital transformation powered by artificial intelligence. To resolve in the best way company’s issues, Dataleon develops innovative automation and adjustable solutions available in the cloud with IA.
Base64.ai is a cloud-based artificial intelligence service that instantly and accurately extracts text, data, handwriting, photos, and signatures from all types of documents, including IDs, driver licenses, passports, visas, receipts, invoices, forms, and hundreds of other document types worldwide. In seconds, Base64.ai discerns the document's type, extracts the relevant information, verifies the results, and integrates them into the customer's systems.
Affinda AI-powered invoice data extraction solution processes documents across a wide range of different formats. Capture data from more than 40 different fields, all fully customizable for your organization’s specific needs.
Affinda’s inbuilt intelligent OCR is capable of reading scanned invoices, and even photos of invoices. Their invoice reader can understand a range of formats, including PDF, JPG, PNG, word, and more. The vast proportion of fields can be automatically extracted with better-than-human level accuracy (>98%).
Form Recognizer is an AI service that applies advanced machine learning to extract text, key-value pairs, tables, and structures from documents automatically and accurately. Turn documents into usable data and shift your focus to acting on information rather than compiling it.
Cradl AI allows you to extract data from unstructured documents such as receipts, invoices, payslips, ID-Cards or practically any other documents. Using the intuitive Cradl AI platform, you are able to build your own custom deep learning OCR model powered by your own data.
Veryfi uses its innovative AI software to provide Intelligent Document Processing. Veryfi AI is pre-trained to extract and transform unstructured data from receipts, invoices, purchase orders, checks, W2s and other business documents into structured data, in seconds, without a human in the loop. Trusted by enterprises and technology companies alike, Veryfi’s AI-based platform is currently in use at hundreds of organizations worldwide.
Klippa offers data & AI consulting and AI-powered SaaS solutions for automating your administrative tasks and workflows based on documents and images. Klippa offers solutions for scanning, expense management, invoice processing, KYC, loyalty, logistics, and back-office automation. These solutions are available as an end to end solutions, but also as RPA components, APIs and SDKs.
Xtracta provides AI-powered data extraction software and OCR solutions to help your organization with all kinds of document automation. Powered by artificial intelligence, Xtracta technology automatically extracts information and captures data from documents, whether they are scanned, photographed, or digital. The technology can be embedded into virtually any software application via our easy-to-use API.
Rossum solves four key steps in document-based processes at once: receiving documents across multiple channels, automated understanding, two-way communication to resolve exceptions, and acting on the data using in-depth integrations. In typical real-world scenarios, Rossum’s proprietary AI engine outranks narrow data extraction solutions in accuracy.
When you need an OCR Invoice engine, you have 2 options:
The only way to pick the right provider is to benchmark different providers’ engines with your data and choose the best, or combine different providers’ engines results. You can also compare prices if the price is one of your priorities, or even compare rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
This is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account to have access to many providers engines for many technologies including OCR for Invoice. The platform allows you to benchmark and combine results from different engines thanks to a standardized response format for all the providers.
For each technology, Eden AI offers the same documentation to use the API. You can use the Eden AI API to call invoice parser engines with a provider as a simple parameter.
Here is the code in Python (doc) that allows to test Eden AI for invoice parser:
There are numerous invoice parser engines available on the market : it’s difficult to know all of them and those who provide good performance. The best way to integrate receipt parser technology is the multi-cloud approach that guarantees you to reach the best performances and prices depending on your data and project. This approach seems to be complex but Eden AI simplifies the task for you by centralizing the best providers APIs.
In this article, we explain how the mapping between the input language and the languages supported by the providers is performed to facilitate access to one of our AI engines.