What is Text Recognition in AI?

Text recognition is a field in computer vision that extracts text from images, physical documents, digital invoices, and copies. Before the advent of AI, text recognition was done with the help of OCR or Optical Character Recognition. Both OCR and text recognition are similar terms that are used interchangeably. OCR is done with the help of software that can recognize the text in images based on the image size, shape, and font. The text identified is matched with a set font library. The quality of text recognition in OCR depends on the quality of the document or image.

OCR technology has improved its detection quality over the years. Now, OCR software can also detect blurry, faded, and even hard-to-read handwritten text. However, the accuracy of the text recognized can vary. The accuracy for typed text is around 98% to 99%. The only way to correct the incorrect data is by manually checking and rechecking the results. However, with AI solutions, this can be done automatically. No matter the volume of documents, AI solutions work on maintaining 100% accuracy of the document.

How does AI text recognition work?

The latest OCR software uses machine learning and artificial intelligence to recognize text. From manual data entry and traditional OCR to AI text recognition, OCR solutions have come a long way.

With the use of AI algorithms, the lack of 1 to 2% of accuracy is fixed. Due to AI in text recognition, text recognition is 100% accurate and faster. The automated processes are capable of understanding and skimming text with more accuracy and are even capable of decision-making. Here is how OCR software normally works:

  • Pre-processing stage

    This is the preliminary stage wherein the document is imported. The pre-processing stage scrutinizes the document for its alignment, size, and standardized inputs. Through this stage, the document is checked thoroughly for any object, which helps the software prepare for text recognition accordingly. The stage also checks for any stains, blurry text, and dust particles to get a more refined document image.

  • Binary conversion

    Once the document is thoroughly analyzed, the binary conversation starts. This stage makes the OCR software easier to detect the characters. Here the document is converted into a bi-level image containing only black and white colors. The white color depicts the background, whereas the black colors are considered as text or characters.

    The OCT systems use two types of algorithms to identify the text block – pattern recognition and feature detection. Pattern recognition algorithm has a fed format of fonts and characters. This serves as a basis for comparison of the scanned text in the imported document. In the case of feature detection, the process is a bit complicated. It divides characters into components. The letters and numbers are identified through their unique features like corners, curves, angles etc.

    For instance, the letter T is identified as two perpendicular lines. The feature detection algorithm looks for a vertical and horizontal line meeting at a midpoint to recognize the letter T.

  • Contextual identification phase

    This is an added feature in advanced OCR systems. Under this solution, OCR systems are trained to identify certain patterns. The feature adds a human touch to document scanning. The system detects sensitive data in the documents and makes a decision accordingly. For instance, a medical report that requires a doctor’s signature may be sent to the doctor’s staff instead of keeping its access public.

clickworker excels in offering Text Recognition AI services, using the strengths of a global workforce to facilitate machine learning projects. Text Recognition, also known as Optical Character Recognition (OCR), extracts text from images and scanned documents, converting it into searchable, editable data. With clickworker, businesses can quickly and accurately label large volumes of such data for training machine learning models, vital for zero-shot learning. By providing comprehensive solutions including data collection, annotation, and validation, clickworker ensures high-quality labeled data at scale, expediting the development of AI models and their market introduction.

Text Recognition for Machine Learning

Use cases of AI text recognition

The application of the OCR system is deeply rooted in many industries. Its use often goes unnoticed. Here is how OCR systems and now AI text recognition have become a part of major industries – finance, banking, hospitality, manufacturing, and more.

AI text recognition in banking

FinTech is a possibility because of AI and machine learning. Banking automation is slowly but gradually making its way. From mobile banking to fraud detection, and security management, AI in finance and banking has helped eradicate manual tasks of document processing, processing checks, managing financial data and statements, and more.

Data aggregation and disintegration have proved to be two main issues that technology can solve. Data aggregation is the process of collecting data from various data sources. Whereas data disintegration is the process of finding the right data from such a mammoth of data when required. This is true for financial institutions. Handling large data and ensuring its security is a task before AI. AI recognition in banking is used for:

Check processing

Check processing is still done manually in a lot of cases. It is sometimes difficult to analyze the check amount as they are handwritten in numerous ways. AI and its neural networks have made it easier to read the amount accurately. It has also made it convenient to process checks faster. Check detection is a three-step process wherein neutral networks play an important role in the second stage of detection. The digits are recognized by neural networks that optimize accuracy and eliminate hassle.

Document processing

OCR and AI have also been tested in document processing. Document processing in banks requires checking ID cards, driving licenses, passports, and many more. The format of each verification document varies from state to state. There is also a possibility of the submission of fake documents by customers.

AI text recognition can solve these problems. The process involves pre-processing the document, scanning, and removing unnecessary noise, design, and color. Thereafter, the use of convolutional neural networks and OCT helps extract the data from the documents.

Some of the documents are hand filled. Such documents are a mixture of typed and handwritten text. It’s difficult for machines to read handwritten text due to the different shapes and sizes of pen strokes. AI in text recognition can also automate and process information from such documents with accuracy.

AI text recognition in healthcare

Hospitals also need to process many physical records that require OCR to process them. From patients’ records, receipts, insurance payments, and hospital records to treatment procedures, the use of documents is vital. AI text recognition software allows patients to scan and upload necessary documents to process claims. Hospitals also make use of OCR to digitalize their records. OCR systems with AI are accurate and faster and reduce the amount of manual work at hospitals.

AI recognition in healthcare is also helpful for seeking medical advice online. Users can upload prescriptions and previous medical records and seek medical devices from trusted sources from the comfort of their homes. Once OCR software is trained to read various document types, the process becomes simpler. The only drawback is the time-consuming and tedious task of training a system.

AI text recognition in airports

Text recognition through AI in airports can be of ample convenience. In airports, it can be used to extract data from passports without manually filling in the passenger details. Furthermore, computer vision at airports is also helpful in getting details of anything, be it aircraft, passengers, luggage, ground staff, ground vehicles, and more. Here is how AI text recognition in airports can reduce tonnes of workload:

Inspection and maintenance of aircraft

Vision inspection, one of the branches of computer vision, can be used for the inspection and maintenance of aircraft. The process involves using images of aircraft to recognize problems. With the use of AI and machine learning, the object is scanned from a distance. This kind of automation examination can detect any defects in the airplane’s body, look for engine failure or leaks, any damage to the wings or fuselage, and much more.

Baggage handling

With the help of OCR, machine vision can read labels or luggage tags. This helps in quick luggage identification and reduces the chances of lost baggage. The technology is already implemented in various international airports like London Heathrow, one of the busiest airports in the world. The information on luggage tags is matched with the information on the airline’s database using computer vision cameras to track the luggage and its owner, making it easier to find misplaced baggage.

License plate tracking

Vision inspection, one of the branches of computer vision, can be used for the inspection and maintenance of aircraft. The process involves using images of aircraft to recognize problems. With the use of AI and machine learning, the object is scanned from a distance. This kind of automation examination can detect any defects in the airplane’s body, look for engine failure or leaks, any damage to the wings or fuselage, and much more.

AI text recognition in airports

With AI recognition, hotels and restaurants can create a digitalized menu quickly. Also, AI OCR combined with computer vision can help create a database of recipes, get nutritional information, details on calorie intake, and more. This will result in greater customer engagement.

The OCR can also help locate the expiry date and product allergy identification. This will also limit food wastage and help industries sort and grade food products. The list is not exhaustive. The use cases of AI text recognition also extend to logistics, retail, the traveling industry, government organizations, manufacturing, and more.

Top AI text recognition tools

The AI text recognition solutions are cloud-based and help businesses streamline their documents, images, and videos. Here are some of the top AI text recognition tools in the market:

  • Google Cloud AI

    Google Cloud AI provides two OCR features: one for documents and another for images and videos. Document AI is used for identifying and extracting data from documents, whereas Cloud Vision is used for detecting text and even handwriting text from images and videos. It provides the flexibility to use the OCR tool to detect text from a document or as an API to embed OCR functionality into applications.

  • Docsumo

    Docsumo is another OCR tool driven by AI to extract, capture and process data from various document types. Docsumo uses machine learning, OCR, and AI to recognize different document layouts. Users can conveniently upload the bulk of documents, which can be digitized using APIs in a matter of time with full accuracy. Some of the unique benefits include document fraud detection and image data capture.

  • Rossum

    Rossum makes use of a neural AI machine to pre-process, capture, validate, and post-process the text on documents and images. It ensures utmost accuracy and does the work six times faster. It also reduces the costs of processing invoices and documents from $13 to $0.05 per invoice. Rossum expands its services to industries and departments. It can also be used for accounts payable, KYC, quality assurance, and supply chain management.

  • Readiris

    Readiris has its own OCR data capture technology to sign, edit, merge, and manage documents. Users can also extract data from images using Readiris. The speed of processing and converting documents is fast, and so is the accuracy.

  • Tesseract

    Tesseract is an open-source OCR that is Python-based. It is conceptualized by HP and is currently managed by Google. It’s a free AI text recognition tool capable of inputting images, identifying equations, and deciphering multi-colored texts. However, it requires a high technical knowledge to use it.

  • Amazon Texteract

    Amazon Texteract is another machine-learning solution that extracts data from scanned documents automatically. Through Texteract, it is also easier to detect physical signatures on an image or document. Many financial companies use Texteract to manage thousands of documents daily, saving costs for new hires.

  • Final Words

    AI text recognition is a boon for many industries, especially those involving a lot of paperwork. There are no limitations to using AI text recognition except that it may reduce the need for manual work.

    The AI text recognition is accurate and faster and extracts data from all sorts of document types and layouts. Some of the advanced recognition tools also perform decision-making. For instance, deciding to protect overly sensitive information in a document by ensuring it’s only accessible by the right person. Thus, preventing fraud and providing security to the data.

    Text Recognition FAQ

    What is text recognition AI?

    Text recognition AI, also known as optical character recognition (OCR), is a technology that allows computers to identify and extract text within images or scanned documents. It transforms the text from a static form into an editable and searchable format.

    How does text recognition AI work?

    Text recognition AI works by scanning an image or document and identifying areas that look like text. The technology then breaks down these areas into lines, words, and individual characters, which are matched against a database of known characters to identify and transcribe the text.

    What are some common applications of text recognition AI?

    Text recognition AI is widely used in a variety of sectors. Common applications include data entry automation, form processing, document digitization, license plate recognition, and assistive technology for visually impaired individuals. It's also used in industries like finance, law, healthcare, and education to extract data from invoices, contracts, medical records, and educational materials.

    How accurate is text recognition AI?

    The accuracy of text recognition AI can vary based on factors like the quality of the original image or document, the font used, and the AI's training data. With high-quality inputs and a well-trained model, the technology can achieve accuracy rates over 90%. However, human review is often necessary to correct errors and validate the results.

    Can text recognition AI handle handwritten text?

    Yes, advanced text recognition AI can handle handwritten text, but with varying degrees of accuracy. The technology for recognizing handwritten text, known as Intelligent Character Recognition (ICR), requires more complex algorithms due to the variability and uniqueness of individual handwriting styles. While accuracy is improving with ongoing advancements in machine learning, it remains less accurate than printed text recognition.