Understand the differences between traditional OCR and AI/ML data capture platforms

OCR (Optical Character Recognition) is more familiar as a technology than it sounds! Anyone who has transformed a scanned image into a text file has used OCR.  Even the humble desktop scanner regularly used in offices now comes pre-loaded with OCR conversion software

When we talk about OCR, it's important to clarify two fundamental concepts: data capture and character recognition.

Data capture and character recognition

Scanning is a common technique for data capture from physical documents. It gives you an image—but the text in the picture is not machine-readable or recognizable by computers. That's where OCR comes in. OCR converts the text in unstructured data into machine-readable text that is searchable and is easy for humans to access and consume.

At a fundamental level, OCR extracts textual information from an image. But it works well when the characters in the picture are machine-printed in standard fonts. If the text in the image is handwritten or printed in a cursive or handwriting font, regular OCR software does not have the intelligence to map the fancy lettering to each alphabet!

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ICR or Intelligent Character Recognition 

ICR gives us this next level of recognition. It handles variations in the shapes of alphabets and correctly recognizes shapes as alphabets even if the variance in the shapes is high. It has the intelligence to understand the context. Imagine that you are reading a printed sheet whose ink has faded in places. If certain words are not clearly visible, you can still guess what the faded word was by placing the rest of the words in a context.

ICR replicates this process and compares a set of possible choices to arrive at the best possible match.

Data extraction: Making information usable and actionable

OCR and ICR recognize characters from images and make them readable by machines, i.e., computers, laptops, tablets and smartphones. Data extraction is a process that goes a step further. It structures the data and makes it actionable.

For example, it reads hand-filled forms and translates them into computer-recognizable form fields. 

Suppose you have a hand-filled form, and you want to convert it into a digital format such as a spreadsheet so that the form fields are recognizable and editable as text.

The first step is to scan the hand-filled form and convert it into a digital image. Next, use an advanced OCR/ICR software to recognize the characters and save them as searchable and editable electronic text formats such as a PDF document or an Excel spreadsheet. This conversion process turns image data into an actionable state. 

What this clarifies is that OCR alone is not enough for advanced document processing. Data extraction (also known as document capture) that turns unstructured or semi-structured data (e.g., forms) into structured data (e.g., documents, emails) is required.

Data extraction is critical for industries that deal with forms regularly. 

Let's take a use case in banking: Banks use specialized check scanners to scan handwritten and signed paper checks and capture the data in a digital format for faster payment processing.  Advanced software reads and extracts the account information, handwritten amount, as well as signature.

Similarly, data extraction automates invoice processing to reduce manual tasks and human intervention in payments and record-keeping.

Making a case for AI/ML-powered OCR

Traditional OCR typically used a template-based approach to extract data. 

A worker marks coordinates on a template or standardized layout of the form. These indicate the location from where the OCR software must extract data and convert it into text. However, this is no good if there are many types of layouts—the manual task of marking the coordinates becomes tedious, time-consuming and sometimes plain unfeasible!

Rule-based techniques are also used—the way it works is that a rule defines the location of the data. For example, it may define the relative position of an element to the area on the scanned image from which the software must extract data. In real-life scenarios, such rules become too rigid and are difficult to set as documents come in all kinds of layouts, and clear patterns may not be discernible.

Another alternative that businesses have tried is to use a rule-based approach but route the task for manual intervention when an existing rule fails to process a document correctly. The worker then corrects the data and adds the variance to the set of existing rules. This approach involves manual intervention, which may slow down the conversion speed and also prove expensive.

AI-powered character recognition and capture platforms

Digital transformation is taking the corporate world by storm. Businesses now demand greater efficiency and cost savings by leveraging automation. 

Artificial intelligence, machine learning, and augmented reality are in high demand, and IDC predicts worldwide spending on artificial intelligence systems will reach a whopping $57.6 billion by 2021.

Advances in AI/ML (artificial intelligence and machine learning) have transformed OCR from an uninteresting, vanilla technology into a transformative, coveted technology enhancement.

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AI for automating document-oriented processes

Every business receives documents from multiple sources and in varied formats. AI/ML data capture platforms automatically identify the document type, sort documents by set rules, locate the relevant data, extract it, and then route the processed data into the appropriate enterprise systems such as the company's ECM or ERP system.

Machine learning creates self-learning models that use previous data to discern patterns and automatically generate rules instead of bringing in human workers to create rules manually. The more data the mode processes, the better its performance becomes. It effectively impacts the organization's bottom line. It reduces manual administrative work, improves organizational efficiencies, saves costs, and makes employees more productive by freeing up their time from mundane, mechanical tasks.

AI-powered data capture platforms have the potential to automate document processing and eliminate manual error-checking and verification tasks. 

Release data trapped in your business documents! 

Organizations often use tedious, time-consuming manual processes to extract valuable data and add value to their products and services. Unlock the power of data with intelligent document processing solutions from DRS.

At DRS, our intelligent data capture solutions leverage the power of AI/ML to transform your content management and document processing.

Talk to us for a quote on AI-powered data capture solutions.