Due to the increasing interest in machine learning and the use of optical character recognition (OCR), many business owners are looking to implement this technology in their operations.
What is OCR?
Optical character recognition is a technology that enables businesses to quickly and easily convert any type of image into text data, which is machine-readable. It is commonly used in various applications such as business process automation and business flow optimization. Its output is used in electronic document editing and storage. It can be used in the development of various machine translation and cognitive computing technologies.
Stages of OCR
The process of converting an image into text is carried out in three phases: image pre-processing, character recognition stage, and post-processing stage.
Step 1 – Checking Image Pre-Processing and Document Type
One of the most challenging aspects of text recognition is identifying different types of documents. This is because each document template has its own unique set of entities and values. To perform properly, OCR software must be able to identify these documents and run the correct pipeline based on their predefined structure.
The image pre-processing step is carried out to remove noise and improve the contrast between the text and the background. This step also helps in improving the recognition of dark and light areas. After converting the image into a black and white version, the program will analyze the document for these two areas. The program will then identify the light and dark areas as the background and characters, respectively.
Step 2 – Character Recognition
Pattern recognition and feature detection algorithms are used to identify the characters in the image. They then create sentences and words based on the data they collect.
One of the most common methods of character recognition is pattern recognition, which is a method that uses the data collected by the program to identify the text and images in the document. This method works well with typescript since it only finds the matches between the samples and the text in the system.
Feature detection is the ability to identify new characters by implementing rules related to their features. For instance, if there are multiple curves or lines in the comparison symbol, the program will be able to recognize these characters.
Another common feature of feature detection is the ability to identify new characters by implementing rules related to their features. This method is usually performed by using neural networks or machine learning. The goal of this method is to find the closest match between the images and the text in the system.
Step 3 – Post-Processing
Once a symbol has been identified, it is converted into an output that can be used by computer programs for further processing. Unfortunately, the output of various image recognition and OCR-related technologies has a lot of false positives and noise. To minimize these, the output will be filtered by removing false positives and ensuring that the program is working properly.
Through statistical data, the system can identify various errors that occur during the process of character recognition. These errors can be caused by the similarities between words and characters.
OCR Business Cases
Due to the accuracy of text recognition techniques, which are mainly based on machine learning, businesses can now create effective solutions that can address various business challenges. Some of the industries that rely on these solutions include healthcare, retail, and security.
In these industries, the use of OCR is mainly used to check the answers to various tests and perform other tasks such as searching through photos and street signs. Security teams are also known to use this technology to process documents such as driver’s licenses and IDs. For each case, a completely different solution is used.
Hardware for OCR
The hardware used for text recognition is usually a scanner or a camera that’s attached to your phone. The software that is used for this process is mainly responsible for extracting text from an image. The hardware is also used to take an image of a document and then convert it into text. The software’s role is to provide the eyes with the necessary information to interpret the data.
Mobile devices are now capable of handling the task of OCR by providing a full-featured scanner. Most of the time, the software that’s used for this process uploads images to a server, which then returns the output to the client.
Due to the increasing number of AI and machine learning projects, the use of optical character recognition techniques has been growing in various industries. Although it’s not 100% accurate yet, its use cases are still growing due to the advancements in these technologies.