Optical Character Recognition (OCR) converts text and handwritten documents into digital PDFs and online documents. OCR has the power to generate machine-readable text from many documents in different languages. This not only saves online businesses the cost of investing in a larger workforce but also makes the customer onboarding process streamlined. The total market size for OCR-related technologies is estimated to top $5.27 billion by 2025.
Functionality of OCR
An OCR software works by breaking components of a document into features and processing them to extract relevant information.
Preprocessing
Different font styles can cause problems in data extraction that’s why preprocessing is required to provide better character recognition accuracy. Some techniques to perform preprocessing include:
De-skew
The document is properly aligned horizontally and vertically to remove redundancies so that it can be properly scanned.
Binarization
The RGB image is converted into a monochrome, often called a binary image – consisting of two primary colors, black and white. These images can be easily read in the later stage of feature extraction since they require lesser computation.
Line Removal and Word Detection
Lines and irrelevant fields from the document are removed to eliminate potential errors. The shapes of different characters and words are taken into account to divide them into specific recognition groups.
Character Segmentation and Normalization
The purpose of segmentation is to divide an image document into different characters. In the case of a text document, the OCR, segmentation is applied at the character-level. The scale and aspect ratio is also normalized to streamline feature extraction in the next step
Feature Extraction
In this stage of data processing, the Optical Character Recognition system treats each character on the document as a feature vector – which represents information regarding that specific character. Feature extraction can be performed by using any of the following two methods listed below:
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The feature detection algorithm analyzes the number of strokes and lines in a certain character.
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The second approach focuses on picking up the whole character instead of smaller components like lines for pattern recognition.
Post-processing
In the final stage, a set of corrections are applied to the extracted data to reduce possible errors. If the output data produced belongs to a single lexicon – a set of possible words allowed in the document – the accuracy of the OCR engine can be significantly improved. Due to advances in OCR technology over the recent years, online OCR libraries are available for free on the internet which can be used to overcome the problem of limited lexicons and improve accuracy. Below are the two main things which post-processing incorporates:
Removing Possible errors
OCR used the nearest neighbor method to detect possible combinations and instances of words. For instance, the phrase, Washington DEC will always be corrected to Washington D.C. because it is always used that way.
Correcting Grammar
Proper use of grammar can determine the level of accuracy in a document. The type of language can be identified using identifiers like verbs, adjectives, and nouns specific to that certain language. The Levenshtein algorithm is used to enhance accuracy in OCR engines as well.
To wrap it up, Optical Character Recognition Technology uses preprocessing techniques to improve character recognition and prepare data for feature extraction, where it is analyzed using a set of techniques for pattern recognition. The final stage incorporates the correction of potential errors to improve the overall output accuracy of the digital document.
OCR uses preprocessing techniques on the extracted data, performs feature extraction, and corrects potential errors to improve the overall accuracy of the output document.