Optical Character Recognition, or OCR, is a new AI technology that allows computers to recognize and translate physical documents into editable digital text.
The role of OCR currently has a crucial role in information technology, because with OCR, the process of scanning documents, processing data, and writing recognition becomes more practical.
As a result, we must comprehend the process of Optical Character Recognition, which must pass through multiple complex steps, starting with image acquisition and up to post-processing.
So, what are the steps in the optical character recognition process?
1. Image Acquisition
The first step in optical character recognition is image acquisition. In this step, a physical document or image containing text should be captured using hardware such as a scanner or camera.
That image is then converted into a digital format that can be accepted by OCR software. Try to take images with good quality, because if the quality is poor, it can hinder the character recognition process.
2. Pre-processing
The following step is pre-processing, in which the acquired images are retouched and adjusted to make them more recognizable. Pre-processing involves various techniques such as image cropping, contrast enhancement, and noise removal.
The purpose of pre-processing is to improve image quality and make it more suitable for subsequent processing.
3. Image Segmentation
Image segmentation is a key step in the optical character recognition process. In this process, images that have been corrected in pre-processing are divided into smaller parts, especially images that contain text.
This process aims to identify the correct location from the characters and text in the image. Segmentation is the most complicated step because images can contain various types of text with different sizes and fonts.
4. Character Extradition
After successfully segmenting an image, the next step is to extract the character. The OCR device attempts to identify individual characters in every image segment. Recognition of character patterns is done based on visuals such as shapes, lines, and angles.
5. Image Classification Process
At this stage, the extracted characters are analyzed and classified into relevant text characters. This process relies on a statistical model and machine learning, which have been trained to recognize characters based on the machine’s visual representation.
Character classification has become a key step in the optical character recognition process because classification accuracy has a substantial impact on the final result quality.
6. Post-processing
The last step in the optical character recognition process is the post-processing step. Text that has been through previous steps requires cleaning and an additional formatting process to ensure that the results are truly perfect.
This step consists of various processes such as removing misidentified characters, correcting spelling errors, and formatting text according to user requirements.
Texts that have been grouped into various segments previously are reassembled into a complete document that can be edited.
Optical character recognition processes in the financial industry
That’s six steps you need to know to understand the optical character recognition process. Optical character recognition (OCR) continues to develop with technological advances; its role is increasingly important in various industries, including finance.
The use of optical character recognition (OCR) in the financial industry, especially in the accounting field, is very beneficial for automating the recording of transaction data and analyzing financial data.
Formerly, transaction recording processes such as purchase invoices, sales, and shopping receipts were manually recorded to an accounting journal one by one. But now, with OCR, we can do this automatically by simply downloading the invoice image.
One of the most trustworthy optical character recognition (OCR) software currently is OCR+ from Fintelite. OCR+ Fintelite can not only recognize physical or digital document data, but it can also automatically provide financial analysis results based on machine learning that have been collected according to business needs.
