INTRODUCTION

When it comes to the modern world images are becoming a wide source when it comes to information share. Most of the logistics and identification is done using images in the modern times and with this modernization huge strides are being made in collecting data which the images contain. In the field of computer vision there are many ways we can detect and extract relevant data from images.

If we are dealing with images like images of invoices, receipts, passports or driving licenses we can use a pre trained OCR model and extract the required data from the image.

But Extracting data from an image has its fair share or problems to be dealt with we can only use an image to text converter which might do the trick, but it has its downside. Using image to text-based systems or features can give us the output but the formant and the alignment of the text will be different and then the data must go under a lot of post-Processing before we can have a desired or optimal output so to avoid that and have an easier approach to the problem statement here’s a process we used.

Image to text- Online image to text converter

To make the OCR more accurate and to tackle problems like alignment and format of the text we are using an AI driven OCR system where the work of the AI is to properly identify the fields of the images. Like in case of passports the name, passport number, nationality etc. or in invoices the company name, the person billed to, invoice number etc. the work of the AI is to identify the respective fields and then pass it to the OCR for detection this significantly decreases the chance of the format and alignment going wrong which saves us form the Post-processing of the Data.

The first initial steps of data extraction if the preparation of data, in the initial stages the data we used to train the AI model had to be Pre-Processed so that the model works at its optimal state and gives us the most accuracy. In the pre-processing stage the images are formatted according to the models needs and keeping the efficiency in mind. Pre-processing includes converting the image into Grayscale and then adjusting some thresholds and sometimes corroding it this process completely depends on the quality of the entire dataset which is provided.

After the data is well prepared the AI model is trained based on the Labelled Dataset, each model which is prepared can be made unique according to the requirements of the Client, extraction of identity data or be it extraction of tables every problem can be dealt with the proper training and optimization of the model.

After the model is trained then it undergoes the process of test and evaluation here the limit of the model is tested based on the test result the accuracy of the model is determined as well as if there any tweaks are needed to make the model even better the testing phase is completely dedicated towards the improvement and accuracy improvement of the model.

The final stage of the data extraction process is the usage of OCR (Optical Character Recognition) system in the backend which can extract the data from the files and store them into a database in the form of JSON or any other file format the client wants, the min work the OCR is to correctly identify the characters which are predicted by the model it does an IMAGE to TEXT conversion and then it stores the text in a particular file format.

HOW AI-powered OCR system extract data from IMAGES?

Well, the above process takes us through a journey of the process of data extraction from images. This process gives its users a lot of customization options for the users can set self-made validation options, can set rules for only specific text or of a specific language and many more the possibilities are quite limitless and with passing of time computer vision algorithms keep on developing thus increasing the accuracy and efficiency of these Systems.

So if you think to give doc extraction a try, please feel free to get in touch.

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