Infrrd AI Enabled Intelligent Document Processing for Complex Documents

Infrrd’s AI-enabled platform transforms invoice management by easing the traditional error-prone process. It holds the capability to analyze, extract, and validate data from invoices of different formats and layouts. Additionally, the platform delivers extracted data into any pre-existing tool.  Click here to know more:  https://hubs.ly/H0kyVvd0

It’s a known fact that P&ID’s diagrams are of great importance to the oil and gas industry. They hold critical information concerning the layout of an engineering process flow along with the interconnected types of equipment. Over a period of time, the landscape of handling P&ID diagrams has witnessed a drastic change. With digitization in place, several vision problems were observed such as resolution, orientation, and the existence of noisy textual information. Intelligent Data Capture

Infrrd  came up with a unique approach. Our  ‘ Intelligent Data Capture’ (IDC) encapsulates advanced AI-enabled capabilities such as Machine Learning and NLP to trawl through reams of documents to detect, analyze, and classify tables. But it doesn’t end there. Next, the data is captured in digital format, which routes data throughout the business environment for future purposes. Originally published at  infrrd.ai

Optical Character Recognition (OCR) tools have come a long way since their introduction in the early 1990s. The ability of OCR software to convert different types of documents such as PDFs, files or images into editable and easily storable format has made corporate tasks effortless. Not only this, it’s ability to decipher a variety of languages and symbols gives Infrrd OCR Scanner an edge over ordinary scanners. However, building a technology like this isn’t a cakewalk. It requires an understanding of machine learning and computer vision algorithms. The main challenge one can face is identifying each character and word. So in order to tackle this problem we’re listing some of the steps through which building an OCR scanner will become much more clearer. Here we go: 1. START WITH OPTICAL SCANNING:   Consider the idea of putting together a good optical scanner, to begin with. With a scanner, one can capture an image of the original file or document. Remember to select an opt...

Data analysis without data preparation it is a myth. Unless we feed the right data in a proper format, Machine Learning algorithms won’t be able to solve our problem. If we give one wrong input then we end up where we started. So it’s very important to understand what data preparation is and how one can do it. Data, in its original form, may have a lot of missing pieces or disarrangement. Through data processing, one can modify this raw information from a specific database to a format which is understandable and learn able by the machine. Mentioned below are the ways that, we at Infrrd, employ in preparing our data. Data selection: It is necessary first to identify the type of data we are going to be working with. One has to keep in mind whether the available data will be able to address an existing problem or not. We keep certain factors in consideration before selecting the data: Data should not be of low quality: Low-quality input= low-quality output. Dataset is not error-r...

Whether you’re presenting an annual report, comparing sales figures, or highlighting a trend, visual representations such as graphs or charts are a great help to understand data elements quickly. However, in today’s hyper-interactive world, it’s hard to understand why data still continues to be represented as colorful graphs. These data trapped visual representations that cause restrictions in harnessing the data or make better decisions. With an increase in document volumes and a growing number of layouts, graph extraction has become a complex process; its optimization is a struggle for many organizations. Manual data retyping is prone to up 90% error, is time-consuming, and is therefore not scalable. This grinding process also requires significant rework and is an area that could benefit from   AI-enabled automated platforms . Many enterprises already understood this and are ready to jump right into automated solutions, implement them into their business processes, and reap...

I spotted another interesting question on Quora related to  machine learning & OCR , here’s my answer: I will give you a consultant’s answer – you may not like it but here goes – “It depends”. The ‘best’  OCR extraction  method depends on the context of what you are trying to extract. My guess is that you are not talking about the OCR process itself. But, rather how to extract features out of the text that OCR spits out. ​There are two broad approaches for extraction depending on whether you know the kind of data you are dealing with (invoices, tax docs, grocery labels, etc) or you do not: DOMAIN-BASED OCR EXTRACTION This approach helps when you know beforehand the kind of data extraction you are after. Let’s say you were trying to extract features of wines from a set of wine ratings and notes that you have OCR-ed. Before you can do the feature extraction, you may consider running topic modeling algorithms on a large collection of existing wine notes to fig...