Machine learning in text recognition and data extraction
[vc_row pix_particles_check=””][vc_column][heading title_color=”heading-default” title_size=”h3″ position=”text-left” title=”What is machine learning?” css=”.vc_custom_1647845385085{padding-bottom: 20px !important;}”][vc_column_text]A branch within Artificial Intelligence, Machine learning allows computers to learn and improve independently without explicitly coding the rules. Machine learning is about building computer programs that can learn independently from past or real-time data.
The machine learning model performs data analysis on top of the observations that it is trained to note, to seek trends in evidence and create better choices based on provided indicators. The fundamental goal is that computers will learn independently, without human involvement, and take decisions accordingly.
This application of machine learning when integrated with optical character recognition (OCR) becomes the basis of text recognition and data extraction.
Thus, this combination of tech stacks brings immense opportunities of computer-based decision making. This makes up for the basis of automation using machine learning and text recognition.
So then, let us understand some basics of Optical Character Recognition.[/vc_column_text][vc_column_text]A branch within Artificial Intelligence, Machine learning allows computers to learn and improve independently without explicitly coding the rules. Machine learning is about building computer programs that can learn independently from past or real-time data.
The machine learning model performs data analysis on top of the observations that it is trained to note, to seek trends in evidence and create better choices based on provided indicators. The fundamental goal is that computers will learn independently, without human involvement, and take decisions accordingly.
This application of machine learning when integrated with optical character recognition (OCR) becomes the basis of text recognition and data extraction.
Thus, this combination of tech stacks brings immense opportunities of computer-based decision making. This makes up for the basis of automation using machine learning and text recognition.
So then, let us understand some basics of Optical Character Recognition.[/vc_column_text][heading title_color=”heading-default” title_size=”h3″ position=”text-left” title=”What is OCR? ” css=”.vc_custom_1647845412149{padding-bottom: 20px !important;}”][vc_column_text]Optical character recognition (OCR) tech is the technology for extracting handwritten or printed text in a scanned image and translating the content into a machine-readable format for data computation such as editing or browsing. Simply put, the image is processed, the characters are recognized, and the strings of text are identified.
Let’s understand this using a simple example. Imagine a detailed court order (Hard copy on paper) that needs interpretation.
With OCR tech stack all you need is to scan all pages. Feed the images. OCR will interpret contents in the document.
Now you can process the intelligence so obtained using various tech stacks like Natural Language understanding to interpret the contents of the court order.
What OCR doesn’t do is consider the fundamental meaning of the text within the image you’re scanning, and it just “looks” at the characters you’d like to convert to a digital medium. For example, scanning a text will acquire and recognize the characters but not the context.
Text recognition is a term that is sometimes used to refer to OCR.
OCR systems constitute a hardware and software combo that converts paper files into machine-readable text. Text is read or copied using hardware such as an optical device or dedicated circuit board, while the software handles additional processing.[/vc_column_text][heading title_color=”heading-default” title_size=”h3″ position=”text-left” title=”How does OCR work?” css=”.vc_custom_1647845477713{padding-bottom: 20px !important;}”][vc_column_text]Scanning or taking picture of a physical document is the first step of OCR, and its processing follows this into a two-color or black and white version. This is only done after the duplication of all pages.
The next step is to identify the light and dark regions of the scanned-in picture or bitmap as characters that need to be recognized, while the light areas are designated as background.
The darker patches are analyzed further to determine if they contain alphabetic letters or numeric digits or special characters. OCR applications use various techniques, but most focus on one character, phrase, or block of text at a time. After that, one of two algorithms is used to identify the characters:
OCR applications are fed samples of text in various fonts and formats, which are then compared and recognized as elements in the scanned page.
Feature detection: To recognize characters in a scanned document, OCR applications use rules based on the attributes of a single letter or number. For example, the number of diagonal lines, crossing lines, or curves in a letter could be a comparison feature. For example, the capital letter “A” might be encoded as two diagonal lines intersecting with a horizontal line in the center.
When a character is recognized, it is transformed into an ASCII code so that computer systems can perform additional operations.[/vc_column_text][heading title_color=”heading-default” title_size=”h3″ position=”text-left” title=”Benefits of OCR” css=”.vc_custom_1647845547833{padding-bottom: 20px !important;}”][vc_column_text]Although OCR has several benefits, it primarily aids organizations in improving the efficacy and efficiency of their work. Its capacity to rapidly search through massive amounts of data is advantageous, especially in office settings where there is a lot of document input and scanning. Below are some of the most significant benefits of OCR data entry:
1. Increased Productivity
OCR software aids businesses in increasing efficiency by allowing for faster data retrieval when needed.
Employees can now devote more time and effort to crucial activities instead of spending time and effort obtaining essential data.
Furthermore, staff does not need to make many visits to the central archives room to obtain essential documents because they may do so without leaving their desks.
2. Cost Saving
One of the most significant advantages of OCR data entry methods is that it allows organizations to automate data extraction at scale and with minimal human intervention. This tool can also help you save money on things like copying, printing, and shipping.
As a result, OCR removes the cost of misplaced or lost papers while also providing further savings as reclaimed office space that would otherwise be required to store paper documents.
3. High Precision
Inaccuracy is one of the most challenging aspects of data entering. Reduced mistakes and inaccuracies from automated data input methods like OCR data entry, leading to more efficient data entry.
Furthermore, OCR data entry can successfully address issues such as data loss. Because there is no human intervention, inadvertently entering incorrect data can be avoided.
4. Storage Space Increased
Enterprise-wide paper documents can be scanned, documented, and catalogued using OCR. This simply implies that data may now be saved digitally on servers, eliminating the need to maintain large paper files.
As a result, OCR data entry is one of the most effective methods for implementing a “paperless” approach throughout the firm.
5. Superior Data Protection
Any organization’s data security is critical. Paper records are easily misplaced or destroyed. Natural conditions such as moisture, vermin, and fire can cause papers to be lost, stolen, or damaged.
Scanned data can be evaluated and saved in digital formats; on the other hand, it does not have this problem. Furthermore, accessibility to these digital records might be restricted to reduce the risk of data mishandling.
6. Documents that are 100% text searchable
One of the most significant benefits of OCR information processing is that it makes scanned documents fully text searchable. This allows experts to look for numbers, addresses, identities, and other criteria that distinguish the paper being searched more quickly.
7. Customer Service Is Significantly Improved
Several inbound contact centers frequently supply clients with the information they require. While some call centers can offer customers the information they require, others will need to swiftly access personal or order-related information to fulfil their requests. In these situations, data accessibility becomes critical.
OCR aids in storing and retrieving materials in a digital format at lightning speeds. Customers’ waiting times are substantially decreased due to this, increasing their overall experience.
8. Allows you to edit documents
Most of the time, scanned papers must be changed significantly when some data must be updated. OCR transforms data to various formats, including Word and other editable formats. This can be pretty useful when materials need to be updated or altered frequently.
9. Recovering from a disaster
One of the most significant merits of employing OCR for entering data is disaster recovery. Even in emergency scenarios, data stored digitally insecure networks and distributed systems stay safe.
In an instance of an unexpected fire or a natural disaster strikes, the digitized data can be promptly retrieved to ensure company continuity.[/vc_column_text][heading title_color=”heading-default” title_size=”h3″ position=”text-left” title=”Machine learning – integrated with text recognition in OCR” css=”.vc_custom_1647845971146{padding-bottom: 20px !important;}”][vc_column_text]One of the myths propagated by existing sources is that OCR does not necessitate deep learning or that utilizing deep learning for OCR.
Anyone who works with computer vision or machine learning, in general, understands that no problem is ever completely solved, and this instance is no exception. On the contrary, OCR produces excellent results only in very narrow use scenarios, and it is still regarded as problematic in general.
It’s also true that there are viable answers for specific OCR workloads that don’t use deep learning. On the other hand, deep learning will be required to make significant progress toward better, more generic answers.
People have attempted to solve the OCR problem using traditional image processing techniques such as image filters, contour sensing, and image analysis, which worked well on narrow, pattern datasets with little variation in alignment, image quality, etc.
However, new methods have been already developed to make our designs reliable to these different variants so that businesses can implement their machine learning applications at magnitude.
In recent years, deep learning algorithms have advanced, reigniting interest in the OCR problem, where neural networks can combine the tasks of locating text in an image with understanding what the text is.
Deep convolutional neural architectures, attention mechanisms, and recurrent networks have made significant progress in this area.
To better visualize it, take the following example: The general pipeline for many OCR designs follows a template: a convolutional network extracts visual features as encrypted vectors. A recurrent system utilizes these coded features to anticipate where each letter in the image text might be.[/vc_column_text][heading title_color=”heading-default” title_size=”h3″ position=”text-left” title=”Conclusion” css=”.vc_custom_1647846056054{padding-bottom: 20px !important;}”][vc_column_text]Many OCR software is dedicated to single-use cases, such as credit card scanning or document scanning. However, OCR can be useful in a variety of situations. Businesses frequently want a combination of OCR solutions, so working with providers who can handle many types of scanning is preferable.[/vc_column_text][/vc_column][/vc_row]
