Robotic Processing Automation

What is Optical Character Recognition (OCR)?

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OCR gained widespread use in the early 1990s for digitizing historical newspapers. Since then, it has undergone multiple enhancements. Present-day solutions use cutting-edge techniques to streamline document-processing workflows. 

Before OCR, it was digitally formatting documents involved laborious retyping of the text. This was time-consuming and led to inaccuracies and typing errors.

OCR converts scanned documents, PDFs, or images into editable and searchable data. It analyzes shapes, patterns, and arrangements of characters in a document, and translates them into machine-readable text.

Here is a step-by-step breakdown of how OCR works:

How does Optical Character Recognition Work?

Step 1: Image preprocessing

When a document is scanned or captured, OCR software preprocesses the image to enhance its quality and readability. This may involve tasks such as noise reduction, image straightening, and contrast enhancement to improve the clarity of text.

Step 2: Text detection

OCR algorithms analyze the preprocessed image to identify regions containing text. This process involves detecting patterns and shapes that resemble characters, words, and paragraphs within the document.

Step 3: Character recognition

Once text regions are identified, OCR software performs character recognition by analyzing the shapes and patterns of individual characters. This involves comparing the visual features of each character against a predefined set of templates or statistical models to determine the most likely character match.

Step 4: Contextual analysis

In addition to recognizing individual characters, OCR algorithms consider the context of surrounding characters and words to improve accuracy. This may involve analyzing word patterns, language models, and grammar rules to infer the correct interpretation of ambiguous characters or words.

Step 5: Post-processing

After character recognition, OCR software performs post-processing tasks to refine the results and correct any errors. This may involve spell checking, error correction algorithms, and confidence scoring to identify and rectify inaccuracies in the recognized text.

Step 6: Output formatting

Finally, the recognized text is outputted in a machine-readable format, such as plain text, rich text format (RTF), or searchable PDF. OCR software may also preserve the original layout and formatting of the document, including fonts, styles, and formatting elements, to maintain the visual integrity of the text.

Types of OCR

Machine learning enables OCR systems to recognize diverse fonts, layouts, and languages, enhancing accuracy and versatility. The algorithms are adept at handling complex document structures and variations, marking a significant leap in document processing efficiency. 

Moreover, machine learning empowers OCR to learn from vast datasets, refine its recognition capabilities, and reduce errors. Modern OCR systems leverage neural networks, enhancing the technology's ability to maintain high accuracy across document types.

a. Printed Text OCR

Printed Text OCR recognizes and extracts text from documents with standard printed fonts. This type digitizes printed materials, such as books, articles, or official documents with high accuracy. Unusual document layouts, poor formatting, and complex backgrounds can pose problems in printed text OCR processing.

b. Handwritten Text OCR

Handwritten Text OCR converts handwritten text into machine-readable characters. This is challenging due to the variability in handwriting styles. Accurate Handwritten Text OCR demands advanced machine learning models trained on diverse datasets of handwritten samples.  

c. Scene Text OCR

Scene Text OCR specializes in extracting text from images captured in real-world scenes, such as street signs, product labels, or posters. Analyzing text from cluttered and dynamic scenes with variable lighting conditions and distortions requires advanced computer vision and OCR algorithms.

Benefits of Optical Character Recognition

a. Increased efficiency and productivity using OCR

OCR technology boosts productivity by automating data entry tasks. It eliminates the need to manually input information from paper documents into digital systems by automatically extracting text from scanned images. 

b. Improved accessibility for visually impaired individuals through OCR

OCR turns printed or handwritten text into digital formats. This allows text-to-speech software to read it aloud, making it much easier for people with visual impairments to access and understand written information.

c. Enhanced document searchability and organization using OCR

OCR transforms static documents into dynamic, searchable files. Converting images into machine-readable text enables quick and efficient searches within documents. Users can quickly locate specific information within large volumes of documents, making document management more efficient and user-friendly.

d. Reduced paper dependence and storage space

By digitizing documents, OCR reduces costs associated with printing, storing, and managing hard copies. It's eco-friendly, saves space, and creates a more streamlined and sustainable working environment.

e. Historical document preservation and digitization

OCR is crucial in preserving historical documents by digitizing ancient manuscripts, delicate records, and old newspapers. This preserves valuable historical content from deterioration and makes it accessible to a broader audience.

Industry-wise Use-cases of Optical Character Recognition

Let's delve into how OCR reshapes workflows and processes in various sectors:

a. Business process automation

OCR harnesses technology to digitize data from images and PDFs . Earlier, businesses grappled with time-consuming manual tasks, leading to errors and inefficiencies. OCR has solved that problem by swiftly converting paper-based documents into digital data. 

Invoices and receipts can be seamlessly processed with OCR—which operational workflows and minimizes the risk of errors.

b. Healthcare

OCR helps with information management in the healthcare sector. Healthcare professionals can instantly access patient information by digitizing patient records and prescriptions. This ensures better record-keeping and quick retrieval of patient history.

c. Education

OCR in the education sector simplifies tasks such as scanning textbooks and lecture notes. In the past, students and educators faced challenges handling vast amounts of printed material. 

OCR has been a saviour by converting these materials into digital formats, creating a dynamic and interactive learning environment. Students can now seamlessly search, highlight, and share information, leading to better educational experiences.

d. Legal

OCR enhances accessibility and easy storage of legal data by making files more dynamic and usable. OCR has simplified processes by converting paper documents into searchable digital files. 

Legal professionals can now streamline case management, enhance research efficiency, and create organized libraries of legal information. Easy and quick information retrieval also improves the speed of legal proceedings.

e. Travel and tourism

In the travel sector, OCR expediates extraction of information from travel-related documents, such as boarding passes, passports, visas and tickets. Airlines, immigration services, and other travel entities use OCR to expedite check-in processes, reduce waiting times and enhance traveler experience.

f. Media and publishing

By digitizing archives and newspapers using OCR, media organizations have successfully improved the longevity of valuable documents. 

OCR provides journalists quick access to historical data and enables repurposing content for a broader audience. It has made historical documents easily accessible in the digital realm, thus contributing to preserving historical knowledge sources.

What’s Next for OCR Technology?

From making business processes smoother to helping the visually impaired, OCR has revolutionized how we access and manage information.

It automates tedious documentation tasks and drives efficiency. OCR is reshaping workflows across industries, from digitizing invoices and receipts to scanning textbooks and lecture notes.

OCR falls in the domain of intelligent document processing (IDP). Advanced technologies, such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), are used to automate the extraction, interpretation, and analysis of information from documents. IDP systems can handle various document types, including invoices, contracts, forms, and receipts, by extracting relevant data and insights to streamline business processes and decision-making.

As deep learning replaces traditional machine learning, OCR technologies will become more competent. Docsumo combines the powers of AI and OCR to simplify text recognition and document classification from large-scale unstructured images. 

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Written by
Ritu John

Ritu is a seasoned writer and digital content creator with a passion for exploring the intersection of innovation and human experience. As a writer, her work spans various domains, making content relatable and understandable for a wide audience.

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