What is an Optical Character Recognition?
OCR – Optical character recognition methods is used to transform a text image into a machine-readable text format. OCR is a method for identifying printed or handwritten text characters inside digital scans of paper files.
Why is OCR Important?
- Most business processes include getting information from print media. Business operations include the use of paper forms, invoices, scanned legal documents, and printed contracts. It takes a lot of time and space to store and handle these enormous amounts of documentation.
- Children and young people encountering difficulty in reading can greatly benefit from digital versions. Consequently, digital text may be used with a variety of software programs that aid in reading.
- Time savings, reduced errors, and decreased workload are the benefits of OCR technology. Hard copies lack choices like compressing into ZIP files, underlining sentences, integrating into a webpage, or sending to an email.
- OCR is predominantly used in Banking, HealthCare and Logistics industries.
What is an OCR Engine?
OCR systems use a combination of hardware and software to convert printed, physical documents into text that can be read by a computer. Hardware, such as an optical scanner or a specialised circuit board, is used to copy or read the text. Advanced processing is often handled by the programme.
OCR technology gained prominence during the early 1990s newspaper digitization project. Since then, technology has evolved significantly. Present-day technology can deliver nearly flawless OCR accuracy.
ABBYY, Azure, Tesseract, Prime, and Transym are a few of the extensively used OCR engines.
How Does OCR Work?
To process the physical form of a document using OCR, we utilise a scanner. After scanning, OCR software converts the page to a black-and-white or two-colour form. The OCR engine looks for bright and dark regions in the scanned-in picture, classifying the dark portions as characters and the light areas as the background.
The following phase involves analysing the dark areas to find numerical or alphabetical digits. We often concentrate on one character, word, or passage of text at a time at this stage. To recognise the characters, one of two techniques is often used: pattern recognition or feature recognition.
Using pattern recognition, we compare and identify characters in the scanned document or picture file by feeding the OCR programme with instances of text in various fonts and formats.
The second process is known as feature recognition, in which characters in the scanned page are recognised by the OCR based on criteria related to the properties of a certain letter or number. These features include things like the quantity of angled, crossed, and curving lines.
An OCR software also looks at the document’s or picture’s structure. It divides the page into parts with pictures, tables, and text blocks. Words are formed by first separating them from lines, followed by characters. The system first recognises the characters and then compares them to a set of pattern pictures. After the programme has checked all possible matches, it displays the recognised text to us.
OCR engine has classified into multiple processing steps
1. Image Binarization:
Eliminating non-text artefacts from the picture document is the main concept here.
Frequently, the papers are stored as a grey level in computer memory, with a maximum of 256 unique grey values between 0 and 255. The grey values in the greyscale palette each result in a distinct colour. We must go through this procedure several times for various colours if the document picture must convey any information. A binary image is more beneficial as it can shorten the time required to extract the desired portion of the image.
A pixel turns white if its grey value exceeds the threshold. In a similar way, pixels turn black when their grey value falls below the threshold.
2. Classification by Styles:
Finding the typeface used to write the text is the goal here. Determining the font’s identity involves determining if the text was generated by hand or, in certain situations by a particular individual. One aspect of classifying a text’s style is determining its linguistic identity.
When selecting the character recognition method that works best for a given text, writing style identification is crucial.
Selecting a few arbitrary passages from the text to use as test samples is one method of achieving style identification. After that, we transform these test samples into feature vectors that may be used to specifically detect the sample text’s stylistic features.
These feature vectors may now be compared to a database of feature vectors that already exists and can be used to identify a certain language, typeface, or handwriting. In this manner, we can determine the entire text’s style as the feature vector of the test sample’s probabilistic closest match.
3. Classification by Characters
We recognise the individual characters in this last section of the OCR engine. Characters within the picture document are separated out based on the style that was established in the step before (style categorization).
After that, we divide the characters into segments and either recognise them using a recognition model trained on an earlier database (OCR pattern recognition) or by using their geometric qualities to identify them (feature detection).
4. Refinements
By utilising a dictionary to translate nonsensical terms to their closest accurate equivalents, we may significantly improve OCR’s output. This is comparable to the auto-correct spelling option found on many modern gadgets.
The recognised text is refined to the OCR engine’s final output using deep learning and other advanced character recognition models, which consider several other contextual elements.
How OCR is used in Mobile Applications
In mobile applications, OCR refers to the use of technology to identify and extract text from documents or photographs that have been imported from the gallery or taken with a mobile device’s camera. An outline of the common uses of OCR in mobile applications is provided below,
- Developers can integrate OCR libraries using an SDK into mobile applications to incorporate OCR capability seamlessly.
- Popular OCR libraries for mobile development include Tesseract, Google Mobile Vision OCR, ABBYY
- Users can choose photos from the gallery on their mobile device or take pictures using the camera within the app. OCR processing uses loaded documents or captured images as input.
- To recognise an extract text from the image, the OCR engine processes it. After that, the captured text can be used in the mobile application for additional processing or display.
- Languages other than English are usually supported by OCR engines, and OCR capability can be tailored by developers to recognise text in certain languages. Depending on the OCR provider or library selected.
- Developers frequently use pre-processing techniques to improve OCR accuracy and image quality prior to OCR processing.
Developers may efficiently use OCR in mobile applications to extract text from images by comprehending and putting these methods into practice. This will enable a variety of applications, including document scanning, data entry, translation, and more.
QR Codes and OCR
When textual data was encoded into QR codes, OCR features were included onto them. Optical character recognition technology can read data from QR codes, including plain text, contact details, and URLs. To enable smooth data transfer between digital and physical media.
The use of QR codes has extended beyond static text to include dynamic data, including payment details, URLs, and vCard information. The incorporation of OCR enabled the effortless retrieval and application of encoded information.
How AI is introduced in OCR
- Shift from Rule-based to Data-driven Approaches: Historically, OCR depended on rule-based techniques, which had trouble with variability and were not very flexible. With the use of AI, data-driven methods—in which algorithms automatically identify patterns and features in massive datasets and now have become more popular.
- Machine learning models: AI-driven OCR makes use of a variety of machine learning models, including Convolutional Neural Networks (CNNs), which are highly effective in extracting features from photos. These models are highly accurate text pattern recognizers since they are trained on labelled datasets.
- End-to-End OCR Systems: AI-enabled OCR systems frequently use end-to-end architectures rather than depending on multi-stage pipelines. These systems use deep learning models to map input photos to output text directly, streamlining the process and increasing accuracy.
- Integration with Natural Language Processing (NLP): By combining OCR with NLP approaches, systems may comprehend the context and semantic meaning of text in addition to recognising it. The ability to understand documents is improved by this connection.
Conclusion
Optical Character Recognition technology has revolutionised the mobile space by enabling users to easily extract, digitise, and edit text on their tablets and smartphones. In addition to increasing productivity and accessibility, its integration into mobile applications has created new opportunities for document management and real-time translation. And the integration of AI with OCR has transformed text extraction by making it possible to create more precise, flexible, and effective systems that can handle a wide range of use cases in different sectors of the marketplace. OCR technology is probably going to become more and more important in determining how mobile computing develops in the future. Elevate capabilities with OCR in mobile app development – inquire about Enterprise Mobility Solutions today.
