Introduction to Optical Character Recognition (OCR)

If you’ve ever spent hours sifting through a stack of documents for content, words, or other information, OCR can be your new best friend. Having the ability to use a PDF reader or other document management tool can save you a lot of time. Most of us in business are continually searching for ways to improve efficiency and streamline operations.

In this endeavor, OCR can be a useful tool. We’ll take a closer look at Optical Character Recognition (OCR) in this piece, including what it is, how it works, and more.

So, what exactly is (OCR) Optical Character Recognition?

Text recognition is another name for optical character recognition (OCR).

Data is extracted and repurposed from scanned papers, camera photos, and image-only pdf using an OCR tool. OCR software extracts letters from images, converts them to words, and then assembles sentences, allowing access to and alteration of the original text.

It also removes the necessity for data entering by hand. OCR systems turn physical, printed documents into machine-readable text using a mix of hardware and software. Text is copied or read by hardware (such as an optical scanner or dedicated circuit board), and additional processing is usually handled by software.

Artificial intelligence (AI) can be used in OCR software to achieve more complex techniques of intelligent character recognition (ICR), such as distinguishing languages or handwriting styles. OCR is most typically used to convert hard copy legal or historical documents into pdf documents, which can then be edited, formatted, and searched as if they were written using a word processor.

When you scan a form or a receipt, for example, your computer stores it as an image file. You can’t modify, search, or count the words in the picture file with a text editor. You can, however, utilize OCR to transform the picture into a text document and save the contents as text data.

How does it work?

As previously stated, an OCR system consists of both hardware and software. The service’s goal is to evaluate the content of a physical document and transform the pieces into a script that can then be used to process data.

Consider postal and mail sorting services, for example. OCR is essential to their ability to quickly process source and return addresses in order to categorize mail more efficiently. The following three approaches are crucial to the program’s success:

1. Image Pre-processing

The technique changes the actual shape of the document into an image, such as a record picture, in the first step. The goal of this step is to make the machine’s representation as accurate as possible while also eliminating any unwanted deviations.

After that, the concept is converted to black and white and appraised for bright vs. dark areas (characters). Using OCR technology, the picture is then split into discrete parts, such as spreadsheets, text, or inset graphics.

2. AI Character Recognition

To distinguish letters and digits, AI examines the image’s dark areas. To target one word, phrase, or paragraph at a time, AI typically employs one of the following methods:

• Pattern Recognition: To train the AI system, technologies utilize a variety of languages, text formats, and handwriting. To identify matches, the algorithm compares the letters on the detected letter image to the notes it has already learned.
• Feature Recognition: To recognize new characters, the system employs rules based on certain character attributes. One trait is the number of angled, crossed or curving lines in a letter.

The algorithm uses criteria based on certain character properties to detect unique characters. The amount of angled, crossing, or bending lines in a character, for example, is one feature.

3. Post-preprocessing

During Post-Processing, AI corrects errors in the final file. One strategy is to educate the AI on a dictionary of terminology that will be used in the paper. Then, to ensure that no interpretations are beyond the AI’s vocabulary, limit the AI’s output to those words/formats.

Benefits of OCR

1. The major benefits of OCR technology are time savings and decreased mistakes. It also allows data to be compressed into zip files, something a real printed page cannot accomplish.
2. Data can be searched using Optical Character Recognition. Scanned files that have been converted to machine-readable files can be stored in any format that can be searched on an organization’s internal server or made available globally on the Internet.
3. OCR is frequently used in conjunction with other artificial intelligence systems. For example, self-driving cars scan and read license plates and road signs, recognize brand logos in social media postings, and recognizes product packaging in advertising photos. Artificial intelligence technology like this aids firms in making better marketing and operational decisions that save money and enhance customer satisfaction.
4. Existing and new information can be converted into a fully searchable knowledge archive. They can also use data analytics tools to automatically process the text database for additional knowledge processing.
5. Optical Character Recognition (OCR) is a powerful tool that can recognize any language script. This capability of OCR, when paired with the Unicode standard and translation software such as Google Translate, allows every scanned and digitized document to be translated into any other language. A benefit that eliminates the need for human translators and their time-consuming efforts.

Use Cases of OCR

The most well-known usage of optical character recognition is converting printed paper documents into machine-readable text documents (OCR). After OCR-processing a scanned paper document, the text can be edited using a word processor like Microsoft Word or Google Docs.

Many well-known systems and services in our everyday lives rely on OCR, which is typically used as an unseen technology. 

Data input automation, assisting the blind and visually handicapped, and indexing documents for search engines, such as passports, license plates, invoices, bank statements, business cards, and automatic number plate recognition, are all essential but lesser-known uses of OCR technology.

By transforming paper and scanned picture documents into machine-readable, searchable PDF files, OCR allows for the optimization of big-data modeling. Without initially applying OCR to documents that do not already have text layers, processing and extracting important information cannot be automated.

Scanned papers can now be incorporated into a big-data system that can read customer data from bank statements, contracts, and other essential printed documents thanks to OCR text recognition. 

Organizations can use OCR to automate the data mining input stage, rather than having personnel analyze innumerable picture documents and manually feed inputs into an automated big-data processing pipeline. 

OCR software can recognize text in images, extract text from photographs, and save text files in the following formats: JPG, JPEG, PNG, BMP, tiff, PDF, and others.

The legal business, which creates the most paperwork, uses optical character recognition in a variety of ways. All printed documents – affidavits, judgments, files, declarations, wills, and so on – can be digitized, stored, and searched using the simplest OCR scanners. 

These methods can be utilized for legal records in other linguistic scripts, such as Japanese and Hindi, as OCR technology expands to languages that do not use the Roman character. OCR technology can provide smooth access to numerous examples from the past for a business that relies significantly on the past.

Applications of OCR

1. Recognizing traffic signs.
2. With a camera, you can recognize number plates.
3. Entry, extraction, and processing of data are all automated.
4. At airports, passports are recognized and data is extracted.
5. Creating a contact list using the information on business cards.
6. Deciphering papers for blind and visually impaired people to be read aloud to them.
7. Making it possible to search via electronic images of printed materials.
8. Creating searchable archives of historical material such as journals and newspapers.
9. Data entry for commercial documents such as checks, passports, invoices, bank statements, receipts, and pro forma invoices, among others.

Conclusion

OCR (Optical Character Recognition) is a technique for scanning and digitizing paper documents. It creates completely searchable digital files from photos, handwritten material, and printed documents.

As these technologies become more economical and available, OCR is a perfect illustration of how AI solutions are driving database modernization.

To summarize, OCR is a fantastic technology with enormous potential. Such instruments are already pretty sophisticated in today’s world. Optical Character Recognition, on the other hand, will improve in the future.

Artificial intelligence (AI) is poised to become one of the most impactful trends in the next years, altering the way we think about information.