OCR solutions have played a critical role in the business world with their ability to recognize text from scanned images or documents. Understanding how they work and what are the different components of OCR available will help choose the most suitable for your organization.
OCR is composed of several components including image preprocessing, segmentation, feature extraction, character recognition, and post-processing. Each component plays a crucial role in ensuring accurate and reliable image to text results.
In this article, we will discuss the different elements of optical character recognition and how they work together to enable high speed and accuracy when transforming images to text.
5 Components of OCR
Over time, OCR has undergone significant advancements, including the integration of cutting-edge technologies like Artificial Intelligence and Machine Learning to enhance the precision of its output. As a result, OCR offers enormous benefits for document taxonomy and processing.
The 5 components of OCR are:
1- Image preprocessing
Image preprocessing is the first building block of optical character recognition and involves making sure the quality of the scanned image of the document is the top before the text recognition process begins.
The main objective is to this phase is to improve the image quality to make it easier for OCR to have a very high accuracy of transformation and includes tasks such as removing unwanted noise or artifacts, enhancing the contrast of the text characters.
There are several techniques to be used to improve the quality of the scanned image including
- Noise reduction: This technique is responsible for removing unwanted elements that may appear in the image such as dots and lines.
- Thresholding: This technique is used to convert grayscale image to binary image by separating the text from background.
- Contrast adjustment: This technique is used to enhance the difference between foreground and background.
Before processing images into readable documents in batches, it is crucial to test the quality of a sample image to ensure optimal results. A senior manager from a Saudi Arabian company contacted me once to check their options after scanning over 15,000 documents, because they were not satisfied with the outcome as they did not test the quality beforehand.
To avoid such a situation and the need to redo the work, it is essential to test the quality of sample images beforehand.
This step involves dividing an image into smaller segments or regions with each containing a single character or word. The goal of this separation is to make it easier for recognizing and converting the text into a digital format.
OCR segmentation is very challenging specially when the scanned document is complex such as construction drawings which contain overlapping characters, and different font size and format.
OCR technology also use different techniques to help them segment the scanned image of the document accurately and identify the characters and words more reliably.
3- Feature extraction
After the image has been successfully segmented into different sections, feature extraction helps in identifying and extracting specific characteristics of the text such as edges, curves, and angles.
Several techniques are used such as edge detection and blob analysis to accurately identify these characters.
4- Character recognition
Once the characters have been identified, they will used to compare and match them against the OCR own databases. OCR usually provide different databases for characters in different languages, and they are usually updated with every release of the product.
Several techniques and algorithms are used to accurately translate and recognize the identified characters from the segmented image such as pattern matching, artificial neural networks, and machine learning.
These advanced techniques have helped increase the accuracy of the ouptut specially when dealing with handwritten text and low-quality images.
Post-processing is the final component of OCR that involves refining and improving the accuracy of the recognized text after character recognition has been completed.
OCR use various post-processing techniques such as language modeling, spell checking, and context analysis to correct any errors or inconsistencies in the recognized text. These techniques help to improve the overall accuracy and ensure that the output text is of high quality and readability.