While the world is transforming to digitization each day, we are more dependent on computers than ever. The handwritten and physical papers are edited electronically, searched, managed and stored in machines. Optical Character Recognition (OCR) is a tool that extracts text from images making this process easier. They convert the text to digitized, machine-encoded format. Likewise, when an OCR is combined with AI, it provides revolutionary results such as data capturing software which simultaneously captures information and comprehends the content as well. Further, AI tools can check for mistakes without human’s help.

Since the 1990s, OCR had its prominence even before combining with AI by assisting businesses with the automation process of physical documents. They used separate software to scan and save documents such as invoices, receipts, etc… Today, the quality of OCR is improved to fulfil the demands of businesses by combining it with AI. The AI helps them in providing templates of documents along with insights.

Data Extraction

The document capture aka data extraction transforms unstructured or semi-structured data into structured data. The OCR can recognize characters from its sources that hold no meaning to machines whereas data extraction structures the data that makes it actionable. Data extraction helps in automating the invoice process so payments and record maintenance are automated.

The rendezvous of OCR and AI

The OCR tools are combined with AI to not only capture data but also capture information and comprehend the contents. The AI checks for any errors without human interruption streamlining fault management. This hybrid tool helps by combining machine learning and computer vision algorithms that analyse the documents during the pre-processing phase specifying what information should be extracted. Then, the OCR engine starts the process of extracting the specified information and translates the same with deep neural networks. They use real-time data to maintain accuracy. While this is the case with AI, without its help, the reports are managed by employees and reviewed by the translator. AI helps us in reducing the burdens of the business.

Deep Learning OCR Models

The text recognition detects and identifies the boxes of the text areas in the image and then identifies each character individually. To detect the words of the document, we use deep learning models such as RCNN, SSD, etc… Whereas to identify the characters in the document we use advanced deep learning models such as the following:

Convolutional Recurrent Neural Network

The CRNN is a three-step approach:

  1. A standard Convolutional Neural Network (CNN) classifies the image into features and “feature columns”.
  2. These columns are then fed into a Long Short Term Memory (LSTM) cell that provides a sequence to identify the relationship between the characters.
  3. The output from LSTM is fed into the transcription layer that uses the character sequence and

Recurrent Attention Model

The RAM model mimics a human eye by catching specific parts of an image to focus upon and gains information from those glimpses. The image is cropped into various sizes with a central point creating glimpse vectors. Each image holds a prominent feature to it. The glimpse vectors are then flattened and passed through a “glimpse network” depending on its visual attention. The network uses RNN to predict the next part of the image which acts as the next input of the glimpse network. The additional parts of the image are explored by performing backpropagation each time to check if the information is good enough to achieve the highest level of accuracy.

Attention OCR

Attention OCR was originally designed for image captioning where CRNN is followed by an attention decoder. The model uses Convolutional network layers to extract image features and encodes them into strings and passes the same to an RNN. This is followed by an attention mechanism that is borrowed from a machine translation model. This attention-based decoder predicts the text in the input image.

Use Cases of Modern OCR

When OCR is considered to be an age-old technology implemented in multiple software programs and hardware devices, the new use cases are evolving each day, testing its potential to the farthest.

Parking Validation: The parking inspectors use a mobile device with OCR to scan the license plates of the parked vehicles to validate whether the car is parked according to the city regulations.

Banking: OCR extracts data from cheques to capture the account information, the transaction amount, and the signature. They also capture data from mortgage applications and payslips.

Insurance: The claim processing in insurance agencies can be automated using OCR and supported technologies.

Healthcare: OCR captures data from scanned reports such as X-rays, history of the patients, treatments and diagnostics, etc…

Legal: Legal firms digitize all their documents into digital formats such as judgements, statements, filings, affidavits, etc,…

Retail: The mobile OCRs help in scanning the serial codes to redeem their vouchers at their convenience.

Modern OCR : A force to reckon

The OCR engines that need to be managed by human users are being transcended by upgraded OCR with AI (Modern OCR) that looks out for any errors. The AI provides them with advanced strategies on data capture and management, The AI OCR tools are a force to reckon with in the digital transformation that aids diverse organizations in automating the process and error checking the documents. This technological advance cuts back cost and increases its efficiency to its fullest potential.

DataMoo AI has been recognized as one of Top 30 Artificial Intelligence Agencies by DesignRush

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