Infrrd AI Enabled Intelligent Document Processing for Complex Documents

Expense reporting has always been a hassle. There are receipts to keep, loads of dates and amounts and details to enter — but people do it, because they like to be reimbursed for the money they have spent on company time. Nobody really likes manually filling in the forms required to get their money back, though.  Infrrd  helped the company unlock data hidden in its clients’ unstructured documents, which meant less of a need to ask end-users to fill out fields. It also meant less opportunity for people entering the information themselves to make mistakes, so less need to spend time fixing them.                                          👉    READ MORE....

Intelligent data capture can achieve up to 95% accuracy and processes 12x faster than manual processing.” Intelligent Data Processing Benefits of Infrrd’s Intelligent Data Capture platform Effort Reduction : The IDC platform is well-equipped with cognitive capabilities that makes self-learning feasible and adapts different layouts, configuration, rules, or templates. This saves a great deal of effort. Time-saving : IDC solutions can process graphs 12x times faster than humans letting employees work on value-added tasks. High Precision : Through self-learning, the module learns to become more efficient and reduces human-ridden errors leading to more accurate and faster results. Increased Productivity : With IDC in place, it’s possible to perform graph extraction in less time with reduced errors. This means less double-checking and reworking tasks, leading to higher efficiency and productivity. Moreover, the business processes will be streamlined, resulting in...

Intelligent Automation Infrrd client wanted to implement a 100%   automated insurance   claim process. Such a process would give them a competitive advantage over other providers. After collaborating on the 100% question, we found the most critical bottleneck in need of a fix was  accurately extracting data  trapped in various  unstructured documents   and images. Infrrd could help them  automate data extraction   with a high degree of accuracy, the claim process could run using  straight-through processing  — with minimal human involvement. We removed the  data extraction  bottleneck for them…Today, they operate right on the cusp of 100%.  <READ MORE >

Infrrd’s current wave of technologies has tested a multilayered extraction model successfully that employs a suite of advanced  AI-technologies  to streamline the operational process. This is a result of a months-long effort in finding the right composition of technical modules. Following details provide a bird’s eye view to the multilayer architecture and its capabilities. 1. Computer Vision As an intense lens can recognize the objects well and spot loopholes, the vision also plays a similar role. It recognizes and identifies the regions of the document that needs to be extracted. It performs preprocessing operations to improve the quality of the document by distinctly identifying the text, images, stamps, handwriting, etc and make it ready for extraction. Also, the document is classified by understanding the structure of the document pages. Intelligent Data Capture At  Infrrd , we’ve done intense research on identifying various objects on a page/image. Our ...

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...

When introduced,  Optical Character Recognition software  was considered to be a boon for businesses. Instead of manually transferring data from records, one could now simply extract data from them. Later they can enter it automatically into in-house systems. We can save hours of manual labor but there remains a caveat. Errors introduced during the extraction process remain in your system. Before we recognize and fix the errors it can stall the work for many hours. OCR scanners are prone to fail when one tries to extract data from old documents. Either the fading of the print or the lack of contrast can lead the software to incorrect recognition of patterns. Additionally, most online  OCR software  is designed to work with English as a language. But not everybody uses English as their first language. If you travel to Europe, the script remains the same but the language differs. A smart engine OCR should be able to pick up this difference in language and adapt its...

The need for making sense of images and extracting meaningful insights from them seems to be rapidly growing. Images are the second most popular content type and there is a vast range of companies that deal with images in one form or the other on a regular basis. This spikes up the need for better  image classification  with increasing accuracy and using automation. At  Infrrd , we dealt with one such use case where the customer wanted us to build a platform for a user to sell his / her watch. In this case, a user uploads an image of the watch he wants to sell. This system eases the user’s work by correctly identifying the watch and auto fill all the details about the image and upload it to the site for sale. Our system needed to do the following: given an image, identify and recommend similar models and finally, classify the watch to the right model. Robotic Automation What most recognition platforms can do: Given an image of a meeting room, they can ide...