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Showing posts with the label process automation

Image Classification- Why Identifying Images Is Not Enough

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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...

Data Digitization In Banking And Financial Services: A Brief Overview

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Old school ways of conducting financial transactions are gradually bidding adieu. Automation, AI, and data analytical tools are taking over to ease the workflow. Everyone in the market wants to move ahead to enhance customer experience, deliver value, and obtain market share. So boarding the train of digital transformation has become the first choice for most companies. Now the question is what does it signify for the present and future of this industry? Adapt and adopt, these are the key terms for most companies who are walking in pace with the technological revolution. Finance being the people first sector is shifting its focus to make their customers’ personal lives better and easier. Businesses are prioritizing greater agility and higher quality for their overall process improvements. Some of the changes they are going through are: Making investments on technology which can support or expand their operating models, thereby making their processes responsive, effective, and digita...

How to Prepare Data For OCR Learning

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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 learnable by the machine. Mentioned below are the ways that, we at Infrrd , employ in preparing our data. 1. 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 err...

How To Improve The OCR Accuracy

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The  OCR  technology has become widely popular today. Existing workflows and business processes have improved a lot after companies started adopting it. Some have even created their own versions of it to achieve better results in terms of productivity. Although, increasing the OCR accuracy isn’t something which can be done overnight but one can definitely try to do so in due course of time. So how can someone fine tune their  Optical Character Recognition  engines gradually? Well, there are different ways to attain this goal. We at Infrrd keep in mind the following tips:    1. Accuracy is achievable at a character level.    2. Accuracy is gainable at a word level. On the character level accuracy, an OCR capability is judged on how often it can recognize a right character, rather than how often it identifies a wrong character. Similarly, word level accuracy means how frequently an OCR identifies a right word. Infrrd   OCR   ha...