What is Optical Character Recognition?


How Artificial Intelligence Gives OCR a Boost

Artificial intelligence is changing OCR tools' capabilities. (OCR) instruments. A field of computer vision , OCR processes images of text and transforms it into machine-readable formats. It uses written or typed text from physical documents and transforms these documents to digital format. In the early 1990s, many entrepreneurs used OCR also known as text recognition for converting physical documents into digital documents. The performance in OCR technology has increased however, demand has grown for greater usability. Recent advances in AI have enhanced the use of OCR due to improved accuracy and faster speed. Thanks to AI humans aren't required at every stage.

OCR and AI: A Benefit to Businesses

Prior to the advent of OCR conversion, the process of changing physical text to digital was a manual process one had to type every document again which was time-consuming and that was prone to errors. With OCR it is possible to convert documents rapidly and with greater accuracy to the original text. When OCR converts the original hard copy into digital format, users are able to edit, format, and search for the document. They can also share it by email, or include it on a website and save it as compressed documents. Naturally, this removes the requirement to store it in physical storage spaces and can be a significant cost saver for businesses who rely heavily on documentation like lawyers or mortgage brokers. When teams use OCR together with AI or machine learning (ML) methods that allow machines that can more precisely convert texts and detect any errors that might occur in the process of conversion. AI can recognize handwriting and also open possibilities for digitizing a greater variety of documents. Handwriting remains an obstacle to AI because of the individuality of each person's handwriting, but with the increase in handwriting training data AI is gaining more capabilities on this front also. To illustrate the AI-powered OCR think of an OCR tool was turning printed invoices in digital files. Let's say that the scanner recognized the invoice's total as $500, but it actually was $5,000. Prior to AI then, the OCR tool would not pick the mistake and it would fall humans to review the document to find it. With AI tools the algorithm will examine all documents, determine that the subtotals for the services offered should be equal to $5,000, and then fix the error without having to oversee the process. This capability of understanding documents helps companies analyze a variety of documents without having to commit human labor on the job. The reduction of administrative tasks is essential to increase employee satisfaction and decreasing turnover. Researchers anticipate demand for AI-powered OCR to increase as these devices are more efficient and cost-effective.

How OCR Works

An OCR system is a mix of software and hardware. The goal of the system is to read the content of a printed document and convert the text in the document into a code that is then used to process data. Consider this in the relation to mail sorting and postal services. OCR is the core of their capability to work quickly when processing address for return and destination to sort mail more quickly and efficient. This is accomplished through three phases:

1. Image Pre-processing

In the first step the device (usually the optical scanner) converts the physical form of the document to create an image - for example, images of envelopes. The aim of this process is to ensure that the scanner will be precise in its rendering as well as to eliminate any distortions that are not needed. The image that is created is transformed to the black and white format and then examined to determine areas with light (background) and darkness (characters). The OCR system could also separate the image data collection into different elements like text, tables or inset imagery.

2. Intelligent Character Recognition

AI examines the dark regions of the image in order to find the letters and numbers. In general, AI targets one character or word or chunk of content at a given time by using some of these techniques:

The pattern recognition team work on the AI algorithm using a variety of formats for text as well as handwriting. The algorithm compares the letters on the image of an envelope to the characters it's already learned to find patterns.

Features extraction: To identify different characters the system employs rules that pertain to specific features of a character. The features could include the number of crossed, angled or horizontal lines as well as curves that characterize the character. For instance, the letter "H" for instance, is composed of two vertical lines, and one horizontal line in between. the machine uses these feature identifiers to determine the various "H"s within the envelope.

Once the machine has recognized the characters, the data is converted into the ASCII code that is used to perform more manipulations.

3. Post-processing

In the third step, AI corrects errors in the final document. One way to do this is to teach the AI on a certain dictionary of words to be included inside the documents. Limit the output of the AI only to the words/format that are relevant to ensure that no interpretations are beyond the dictionary.


Applications of OCR

There are many applications for OCR Training Dataset every business that manages physical documentation can profit by its use. Here are some of the most prominent examples of its use:

Word Processing

One of the first and most popular uses of OCR can be word processing. It allows users to scan documents and convert them to editable and searchable versions. AI assists in ensuring that the documents are converted with the highest accuracy.

Legal Documentation

OCR Data Collection can help you place important legal documents, like loan documents in an electronic database to make it easy to reference. Multiple parties can view and exchange documents too.

Retail

Retailers make use of serial numbers to identify their merchandise. Warehouses and retail outlets robots scan barcodes of products, use OCR to decode serial numbers from the barcodes and then use this information to monitor the inventory.

Historic Preservation

OCR converts historical documents into searchable PDFs that can be searched. This is particularly useful for the preservation of old magazines, newspapers letters, as well as other records from the past.

Banking

Today, you can utilize your smartphone to snap photos of the front and back of the check that you'd like deposit. AI-powered OCR technology will automatically check the check to verify its authenticity and ensure that it's in the right amount hoping to deposit. OCR technology wouldn't be as modern today without the boost of AI. AI when paired with OCR reduces errors, drastically increases the accuracy of conversions and also provides further analysis for documents. The lower administrative and cost burden is an important benefit for businesses looking to gain an efficient way to manage documents.

What GTS Offers?

GTS relies on our expert team to assist you in creating modern models using OCR. GTS is responsible for ensuring that our customer models that utilize OCR are completed successfully with their AI Training Datasets.

Choose the appropriate information for your model, making sure that it corresponds to the kind of data that you're hoping to encounter in the real world. For instance, if creating a model to automate the transcription of receipts, the data you use should comprise receipts that contain the data you're trying to find. Also, your data must be well-rounded, with images taken at different angles, various types of image quality, and so on and so on - especially if the model is going to be applied to content created by users. The correct tooling is essential! Since your training information needs to be complete, the software that you employ to mark the data should be able to work with all kinds of documents.

A Human-in-the-Loop strategy is essential to achieve the success. For the sake of ensuring accuracy in your algorithm it is best not to be relying on AI only. Participating in the annotation process will allow you to spot and fix mistakes prior to the training. GTS is the forerunner when it comes to artificial intelligence (AI) data collection. We are seasoned experts with recorded success in various forms of data collection, we have improved systems of image, language, video, and text data collection. The data we collect is used for Artificial intelligence development and Machine Learning.

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