ID card data extraction is the most important document to extract in a few fields. Somewhere it turns out beneficial especially in banks and insurance institutions. Data extraction from ID cards using deep learning is the easiest method. It results in achieving greater efficiency and cutting costs. In this blog, we will know how to extract documents from different categories of ID cards. Mainly, document extraction deals with the information, invoice, etc. to extract from a pdf or scanned images. To build a technical understanding of how deep learning can improve the quality and solves the problem regarding ID card data extraction.
What is automated data extraction?
Data extraction refers to document digitalization and digital data extraction processing. Automated data extraction is similar to data entry tasks, but it is the digital version. Automated data extraction occurs via software and tools. The document arrives through pdf or scanned images, that need to be extracted.
With the use of software and tools, the document converts from scanned pdf to editable words. Mainly, the data extraction software aims to extract images from docx. or jpg to text file. After data extraction, the software pulls out the documents from the software and enters the information into the system successfully.
ID card Extraction Using Deep Learning
Organizations such as Banks and insurance or financial companies collect an adequate amount of information from the customers. The customer requires to submit some documents to verify their identity. The company also collects some relevant details about the customers. Manual data extraction of ID cards is time-consuming and prone to errors. So, the customer has to present a digital document. Now, the documents will be reviewed to check whether it is fake or not. After all the processing, the data extraction tools extract the name, address, and further information into the software.
Now, data extraction has become easier, frequent, and adopting. The reasons are:
The process is simple, the user captures all information. Next, the user uploads the information in the data extraction software. The data extraction software extracts the data in the form of texts. It also provides the extracted data to be in an arranged format to undergo an identity verification process.
Minimizing of errors
Replacement of manual efforts also results in minimizing the errors. Working with advanced technologies and technical powers has reduced the chances of errors. The software extracts data automatically following repetitive tasks. Automated software reduces efforts given by humans. The requirement of human labor comes in the case of reviewing after the final data extraction of the ID card.
Increase in speed and better efficiency
Using automated technologies with deep learning saves lots of time. It also reduced the amount of cost spent. The automated software extracts bundles of documents; the outcome comes out within a few seconds. It works repetitively which results in increased productivity. The software connects with deep leaning based technologies that result in better efficiency and quality.
Working with digital solutions provides the way to place the digitalized documents into the system easily. Data extraction processes through software such as Docextractor. The software programs in locating the required information from the ID card. The user captures the information and uploads it on the website of the Docextractor. Then, Docextractor extracts the information from the document in the form of an excel template, etc.
Provocations and Faults of Using Deep Learning
Automated data extraction performs its function through software. The software is a machine; it works based on programming. It surrounds by advantages as well as disadvantages. Deep learning is the solution to the entire problem. The researchers and the developers are still trying to promote a perfect model or software that possesses meritorious efficiency and high output accuracy.
- Deep learning trains the software to read and recognize different fonts, layouts, and templates from ID cards. It also trains the software to recognize characters such as large class variations and forms. That makes the character recognition tool difficult to work. Extraction of such documents results in errors.
The ID card also arrives in foreign languages. The extraction of data from Multi-lingual ID cards is complex and troublesome. It happens due to primary research issues and results in poor data extraction.
- Manually, when the user captures images, may face problems regarding orientation and rotation. In the case of cell phones, there is an advantage. The cell phone knows when it is titled and twisted. In that case, the user understands the problem. In the case of capturing manually, skewing occurs. It is the angle at which images of ID cards are taken. An increase in skewing also results in poor results. There are solutions to improve the problem. It consists of techniques such as Projection Profile, Hough Transform, etc.
- There are many verification methods an image goes through before being processed such as an ID card. The image requires many complications when they arrive such as tilting, color contrast, lightning. These complications result in errors. For example, in the case of color contrast of ID card, if a white background contains word printing in grey color, the text will be invisible to human’s eye as well. It will result in poor extraction. To avoid these problems or errors, the images have to undergo pre-processing methods. In contrast, the method arranges the documents based on automated data extraction significances and consequences.
Data Extraction Using OCR
OCR or Optical Character Recognition is the data extraction tool. OCR helps in faster data extraction. It consumes less time. It also extracts data from ID cards in a couple of seconds. In this way, errors become less than before. It has completely replaced human efforts. The development of faster speed and error-free data extraction from ID cards results in increased accuracy and better efficiency.
Equally, it also increases the productivity of the organization. OCR finds difficulty in recognizing specific characters and different formats. The researchers and the developers are working on discovering a perfect OCR model to work with the best accuracy and efficiency. ICR or Intelligent Character Recognition is one of them which fulfills the requirements that OCR lacks.
OCR converts scanned images to editable words. It also extract images from docx. as well as jpg to text file. Using OCR, corrections, and edits are easily made on ID cards. Companies deals with several customer identities. Manually, to find one of them is troublesome. Data extraction from ID cards using OCR has become easier. The required identity can be easily searched or found. If you want to digital your ID card, upload an image to Docextractor website. With the use of OCR, it will extract documents within a few seconds by following better accuracy and quality.
Methods of Improving OCR
With the use of deep learning, data extraction using OCR has achieved a different state of performance in data extraction.
Popular deep learning techniques
OCR shows errors in most cases. To minimize such problems OCR uses deep learning techniques. The data extraction from documents using object detectors has become easier. The technique trains the software to recognize the specific characters such as foreign languages, numerical, etc., and the different formats, layouts, forms, and variations easily.
The technique teaches the software to recognize the object in the image. Then, the software easily extracts the documents. The techniques are Faster-RCNN, Mask-RCNN, YOLO, SSD, and RetinaNet helps OCR in data extraction from ID cards. Software such as Docextractor utilizes deep learning techniques to provide the customers a better quality work.
There are various neural networks and each of them works by following different methods. For ID card data extraction, it requires intersection processing. Intersection processing signifies both images which need to be captured and texts which need to be recognized. CRNN is the basic convolution network, the next level is known as the feature layer.
The feature layer divide into feature columns. The feature columns divide into deep-bidirectional LSTM which brings out a sequence and relation between characters. Now, CRNN has slowly become a tool for text recognition. It extracts data by maintaining 70% accuracy. By using these various techniques OCR improves the quality. The data extraction software such as Docextractor also uses neural networks for data extraction from ID cards using OCR.
Spatial transformers network removes the variations from the image. The network understands the important portion and focuses on that. The network localizes the images and gives the parameters we want to apply to them. Using the network provides corrections easily on the ID cards. The neural network consists of a net, a grid generator, and a sampler. The grid generator helps in localizing the point to transform. The sampler works to bring out the transform feature map. This is how the OCR improves in data extraction from ID cards.
Attention OCR is the combination of CNN and RNN. It works with the mechanisms of both neural networks. The layer of the convolution network uses to extract encoded image features. Mainly, the image passes into the CNN feature extractor and results in a single feature map. It results in better accuracy.
Data Extraction Using IDP
IDP or Intelligent Document Processing is another tool used for data extraction. IDP is known as advanced AI technology. It processes data extraction from ID cards faster than OCR. IDP also results in a reduction in errors. It links with deep learning to produce tasks with improved quality and better accuracy. IDP connects with the entire system. Besides, it does not require human efforts except for reviewing purposes. It increases productivity much better than OCR. It also undergoes pre-processing methods to improve the quality.
With the use of deep learning technologies, IDP or Intelligent Document Processing is termed advanced technology. ID cards arrive in different formats and languages as well. It uses technologies such as Natural Language Processing (NLP), Computer Vision, Deep Learning, and Machine Learning (ML) to improve accuracy. Utilizing the techniques IDP easily recognizes specific characteristics such as signatures, foreign languages, phone numbers, etc.
Not only that, but it also extracts the documents with different formats, layouts, variations, and forms. The banks and insurance company collects essential information or identity records from the customers. The information requires to be secured. For that, the automated technology is encrypted with cloud-based security. It is strongly built coding. It keeps the hackers away from the system. Data extraction from ID cards using IDP also results in better accuracy and increased efficiency.
Benefits of Using IDP for ID card Data Extraction
IDP is considered a smart technology. It has been updated with some updated technologies that initiate its working. When IDP connects with the software, it results in faster data extraction. It minimizes errors. Working with deep learning technologies, IDP has improved its working phenomenon. It also increases the efficiency of work that leading to increasing productivity. Therefore, the chance of errors minimizes the rate of accuracy rises. Data extraction from IDP using smart technology such as Docextractor is easy. It provides smooth and clear data extraction to the customers.
IDP has many automatic options. The document arrives via mail, pdf, etc. The documents need to be extracted. The document also arrives in unarranged formats. To avoid the problem, IDP has secret skills. IDP is capable of auto-categorizing, auto-classifying, and auto-cropping the data in the pdf. It is also capable of finding out the specific information required. The entire works automatically. Data extraction from ID cards will be easier. It will not require many pre-processing methods. Docextractor is the software that extracts data in the same way by providing better options. Besides, it provides the facility of extracting the data according to the customer’s requirement such as an excel template.
Data extraction from ID cards has become easier due to automated software and tools. ID card contains information in different languages. The automated tools manage to recognize the data and extract out quickly. It has been discussed how deep learning replaces manual efforts. It also changed the entire system and controls it to run easily. Automated technologies have reduced the paper loads on employees and the risks of losing essential documents.