What is OCR (Optical Character Recognition)?
Optical Character Recognition (OCR) refers to the process of converting text images into machine-readable text formats. Imagine when you scan a form or receipt—the computer saves the scanned content as an image file. At this point, you cannot directly edit, search, or count the text within the image using a text editor. However, through OCR, you can convert this image into a text document, allowing the content to be stored as text data.
Why is OCR important?
Modern business workflows often rely on printed media to obtain information. Paper forms, invoices, scanned legal documents, and printed contracts are all part of daily work processes. Processing and storing such a large amount of paperwork requires considerable time and space. Although paperless document management has become a trend, scanning documents into images still presents challenges. This process usually requires manual intervention, which is not only cumbersome but also time-consuming. Additionally, the image files generated during digitization may conceal text that ordinary word processing software cannot handle efficiently as it does with text files.
OCR technology addresses this problem. It converts the text in images into text data that other business software can analyze. Enterprises can then utilize this data for analysis, optimize operations, automate processes, and enhance work efficiency.
How does OCR work?
An OCR engine or software completes the conversion through three main steps:
Image Acquisition
First, a scanner reads the document and converts it into binary data.
Data Preprocessing
Most OCR technologies initially perform a series of processing tasks on the scanned image—such as resizing, normalization, and noise reduction—to improve the quality of the input data.
Text Recognition
Once the OCR system identifies the text areas, it decomposes those specific regions to recognize individual letters and words. In this process, individual characters are called "glyphs." When recognizing glyphs, the system may match them with previously stored glyphs or detect shape features (like loops, crosses, dots) to "guess" based on unique patterns. Recognizing handwritten content is particularly challenging.
Pattern Matching
The system extracts character images (known as glyphs) and compares them with stored similar glyphs. Effective pattern matching works well when the stored glyphs closely match the font and size of the input characters. This method is ideal for scanned documents entered using known fonts.
Feature Extraction
Glyphs are broken down into various features such as lines, closed loops, line directions, and intersections. These features are then used to find matches among stored glyphs.
Post-processing
Errors may occur during the text recognition process due to font variations, noise, or other factors. The post-processing step aims to improve the accuracy of the results. At this stage, the OCR system corrects the text through spell checking and grammar rules—comparing with dictionaries or using statistical methods to check the frequency of different words. Meanwhile, the system may format the recognized text to conform to the desired output style, such as normalizing capital letters, removing extra spaces or punctuation, or formatting dates and numbers in specific ways.
What are the types of OCR?
Data scientists classify OCR technology into several types based on its applications and uses. Here are some primary examples:
Simple Optical Character Recognition Software
A simple OCR engine stores various fonts and text image patterns as templates. The software uses pattern matching algorithms to compare the text image with its internal database character by character. If the system matches successfully, it's called optical character recognition. The limitation of this method lies in the virtually infinite number of fonts and handwriting styles, making quality and accuracy difficult to guarantee.
Intelligent Character Recognition Software (ICR)
Modern OCR systems adopt intelligent character recognition technology, allowing machines to read text like humans. These systems utilize machine learning, employing complex algorithms to train machines to understand and parse text. Known as neural network systems, they break down and analyze the text at multiple levels, combining all analysis results to provide a final answer. Although ICR usually processes only one character at a time, it remains highly efficient and can produce results within seconds.
Intelligent Word Recognition
This system works similarly to ICR but processes entire text images rather than parsing characters one by one.
Optical Mark Recognition
Optical Mark Recognition is primarily used to recognize marks, watermarks, and other text symbols within documents.
Advantages of OCR
Using OCR technology has many significant advantages, including:
1.Searchable Text: Enterprises can convert existing and new documents into fully searchable knowledge archives. With data analysis software, text databases can be automatically processed for in-depth knowledge extraction and handling.
2.Operational Efficiency: OCR software can help integrate document workflows within enterprises with digital workflows, significantly improving efficiency.
3.Artificial Intelligence Solutions: OCR is often a component of other artificial intelligence solutions implemented by many enterprises. For example, they can be used in self-driving cars to scan and read license plates and road signs, detect brand logos in social media posts, or recognize product packaging in advertising images. These AI technologies help enterprises make better marketing and operational decisions, reduce costs, and enhance customer experiences.
Advantages of Deep Learning OCR
Deep learning OCR systems combine all the advantages of large-scale machine learning. They can efficiently process massive data and have strong scalability, making them especially suitable for organizations with large volumes of documents. By combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), they can better understand text context and improve accuracy, even in complex scenarios.
Enhanced Data Analysis
Deep learning OCR can perform real-time processing, allowing instant recognition and extraction of text, which is ideal for scenarios requiring fast data processing. The extracted data can further integrate into analysis and decision-making processes, obtaining valuable insights and promoting real-time business intelligence.
Reduced Manual Data Entry
Deep learning OCR systems cover all the steps from preprocessing to post-processing within a single architecture, significantly reducing reliance on manual data entry. Manual input processes are often time-consuming, error-prone, and costly. By automatically extracting text from documents, the need for human intervention is greatly reduced, accelerating data processing.
Frequently Asked Questions
What is the difference between OCR and machine learning?
OCR is an application instance of machine learning. Machine learning models underpin the technology behind OCR solutions, and the application scope of machine learning extends far beyond OCR.
Is OCR considered artificial intelligence?
Yes, OCR is a manifestation of artificial intelligence technology. However, not all OCR solutions are considered AI. Some OCR solutions are rule-based, utilizing older algorithms, while advanced versions of OCR leverage AI technology to provide faster and more accurate results for images.
Conclusion
As technology advances, OCR is becoming increasingly intelligent, helping enterprises improve efficiency and reduce manual workload. Moreover, OCR combined with artificial intelligence and deep learning significantly enhances the accuracy and real-time processing of information. Whether in business operations, document processing, or data analysis, OCR has demonstrated immense potential. With the continuous progression of this technology, we can expect to see more innovations and emerging application scenarios.XXAI helps you implement OCR in your business by automatically extracting text, handwriting, and data from scanned documents such as PDFs.