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Character recognition technology, which extracts text information from image files and PDFs, is a technical field with consistently high business needs across all industries and sectors. In recent years, the capabilities of AI-based image recognition and natural language processing have significantly improved, and the implementation of AI for character recognition is rapidly expanding.
In this article, we explain the overview of character recognition by AI (AI-OCR), the process leading to identification, and its benefits and drawbacks. We also introduce points to consider when implementing AI in the latter half, so by reading to the end, you will understand the practical points for utilizing AI character recognition.
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【Table of Contents】 |
1. What is Character Recognition by AI (AI-OCR)?
Character recognition by AI (AI-OCR) is a cutting-edge technology that combines conventional Optical Character Recognition (OCR) with Artificial Intelligence (AI).
AI technologies such as deep learning have significantly improved character recognition accuracy. By utilizing AI, it is possible to read handwritten characters and documents with non-standard formats with high precision. Furthermore, it can identify characters by considering the context and relationship between phrases, making it capable of handling typos and idiosyncratic handwriting.
Differences from Conventional Character Recognition
The differences between conventional character recognition and AI character recognition lie primarily in recognition accuracy and adaptability.
| Conventional OCR | AI OCR | |
| Recognition Accuracy | Relatively Low | High |
| Handwriting Recognition | Poor | Good |
| Format Support | Fixed formats only | Supports non-standard documents |
| Layout Analysis | Pre-setting required | Automatic extraction possible |
| Learning Ability | None | Yes (Continuous improvement) |
| Context Understanding | Not possible | Possible |
| Industry Terminology Support | Limited | Supportable via learning |
| Processing Speed | Fast | Slightly Slower |
| Implementation Cost | Relatively Low | Relatively Low |
Conventional character recognition is a technology dependent on specified fonts and layouts. This means formats must be defined in advance. However, it faces the challenge of failing to prevent misrecognition in handwritten characters or irregular formats.
On the other hand, AI-based character recognition achieves high recognition accuracy even for handwritten characters and documents in various formats by learning from massive amounts of data.
Furthermore, character recognition combined with AI does not just recognize characters; it also performs contextual understanding and situational analysis.
AI for character recognition makes it possible to handle complex formatting and non-standard data that was difficult with conventional OCR, thereby achieving automation and efficiency in business processes.
2. Process of Character Recognition by AI
AI character recognition proceeds according to the following process:
- Image Pre-processing
- Character Region Detection
- Feature Extraction
- Character Recognition
- Contextual Analysis
- Post-processing and Output
Each step is explained below.
1. Image Pre-processing
In character recognition, image pre-processing must be performed first. Since acquired images cannot be accurately recognized as they are, pre-processing is carried out using the following methods:
- Image noise removal: Removing unnecessary points, lines, or background graininess generated during scanning or photography.
- Rotation and scaling adjustment: Automatically rotating and scaling the image to correct it into an appropriate shape.
- Binarization: Image processing that converts a grayscale image into only two colors: black and white.
Noise removal sharpens character outlines, preparing the state for the AI to recognize characters accurately. Rotation and scaling adjustments are effective when documents are scanned diagonally or when image sizes are inconsistent.
Binarization clarifies the contrast between characters and the background, allowing the AI to clearly identify character boundaries.
2. Character Region Detection
Once image pre-processing is complete, character region detection is performed.
This makes it possible to accurately detect character regions based on character shape and arrangement, even in handwritten notes or documents with complex layouts.
3. Feature Extraction
After character regions are detected, the process moves to feature extraction, which pulls out characteristics such as character shapes and lines.
First, patterns of character shapes and lines are analyzed from the image. Then, features such as outlines, edges, and curves are extracted at the pixel level.
4. Character Recognition
In character recognition, the AI correctly recognizes and classifies characters based on the information obtained through feature extraction.
The AI has learned from massive datasets in advance. During the character recognition process, the shape and pattern of the input characters are compared and matched against the dataset to extract the closest matching characters.
5. Contextual Analysis
Contextual analysis is the process of providing more accurate recognition results by considering the sequence of character strings and the context. This analysis allows the AI to reduce the possibility of misrecognition and recognize natural character strings that fit the context.
In recent years, methods using LLMs (Large Language Models) such as BERT or GPT to perform more advanced contextual understanding are also frequently adopted.
6. Post-processing and Output
After contextual analysis is complete, the recognized data is further scrutinized and organized into a practically usable format. In this process, post-processing such as spell checking and grammatical correction is performed on the character strings recognized by the AI to minimize misrecognition and input errors.
Finally, the corrected data is output in the format required by the user. It is provided in formats suitable for the specific business task, such as text files, PDF, or CSV, ensuring smooth import into systems or integration with databases.
3. Benefits Brought by AI Character Recognition
AI character recognition offers the following benefits:
- Handwritten documents can be read with high precision
- Can read documents with different layouts
- Reading results can be utilized as data
- Operational efficiency through RPA integration
Each benefit is explained below.
Handwritten documents can be read with high precision
Utilizing AI for character recognition significantly improves reading accuracy. By leveraging deep learning technology, it is possible to read characters with high precision even from handwritten notes or cursive script that were previously difficult to recognize.
Furthermore, AI can analyze the context of recognized characters and words. It can more accurately distinguish between similar characters like "0" and "O," "1" and "I," or "5" and "S," where misrecognition frequently occurs.
Can read documents with different layouts
Conventional character recognition sometimes suffered from decreased accuracy with non-standard documents or documents with complex formatting. However, AI character recognition utilizes deep learning to maintain high adaptability toward documents of various formats.
Even with documents containing mixed layouts and formats, such as invoices, receipts, contracts, and handwritten memos, the AI performs character recognition accurately.
Reading results can be utilized as data
Utilizing AI character recognition contributes to converting information stored on paper media or as images into databases. This enables the automatic processing of documents.
Information converted into databases makes searching and management easy, significantly improving operational efficiency.
Operational efficiency through RPA integration
RPA (Robotic Process Automation) is a software robot technology that automates repetitive administrative tasks on a computer. By linking RPA with AI-OCR, operational efficiency can be significantly improved.
By accurately reading character data from paper documents or scanned images through character recognition and automatically digitizing it, business workflows in RPA can proceed smoothly.
4. Drawbacks to Note When Implementing AI Character Recognition
While AI character recognition has benefits, it also has the following drawbacks:
- Initial and running costs are incurred
- Perfect character recognition is impossible
Each point is explained below.
Initial and running costs are incurred
When implementing AI character recognition, both initial costs and running costs must be considered. These costs vary greatly depending on the scale of implementation and the form of use.
Ensuring an AI model can recognize characters normally requires many initial investments, such as:
- AI OCR software licensing or AI model construction
- Purchase of scanners and dedicated devices
- Training of the AI model
- High-performance servers or cloud services
- Employee training
Furthermore, AI model updates and software maintenance are required as running costs. Re-training models with new data and adjustments for improving accuracy are indispensable. Accurately estimate running costs for long-term operation.
Perfect character recognition is impossible
While AI character recognition boasts very high accuracy, perfect character recognition remains difficult.
Although utilizing AI allows for handling handwriting, complex layouts, and low-quality images, complete character recognition accuracy cannot be guaranteed in all cases.
In particular, messy handwriting or extremely distorted characters may result in more misrecognitions compared to printed text. Furthermore, in multilingual documents where multiple languages are mixed, there is a risk that misrecognition is more likely to occur at the points where languages switch.
5. Key Points for Implementing AI Character Recognition Technology
When implementing a character recognition system utilizing AI, it is important to keep the following points in mind:
- Perform visual checks as well
- Collect abundant image data
- Improve annotation accuracy
Each point is explained below.
Perform visual checks as well
Although AI-based OCR has high precision, there are limits to complete automation, so visual checks by humans are also indispensable.
Especially when industry-specific complex documents, special fonts, or handwriting are included, misrecognition can occur even with AI.
Particularly in important contracts or legal documents where accuracy is required, it is recommended not to rely too heavily on AI automation and to use human verification in conjunction.
Collect abundant image data
Since AI improves character recognition accuracy through repetitive learning, the more data it learns from, the more it becomes capable of handling various situations. Therefore, it is important to collect a wide range of image data, including different fonts, handwriting, complex layouts, and various backgrounds.
Improve annotation accuracy
Annotation refers to the task of attaching accurate labels or tags to learning data for AI models. If annotation work is not accurate, the AI will learn from incorrect data as a "teacher," potentially decreasing character recognition accuracy. Therefore, annotation quality is a crucial factor that determines character recognition precision.
When performing annotation, it is necessary to accurately specify the character regions within an image. Additionally, information regarding characters and context must be correctly labeled during annotation. Using diverse datasets encountered in real business scenes and performing detailed annotation are key points for improving accuracy.
In this way, improving annotation accuracy is expected to significantly increase the precision of AI character recognition.
However, because annotation work requires significant time and cost, developing efficient work methods such as the utilization of annotation tools is also a challenge. If personnel and man-hours cannot be secured in-house, outsourcing to an external specialist will likely be more efficient.
6. Summary
AI character recognition possesses high precision and flexibility beyond conventional character recognition, contributing to efficiency in various business scenes. The ability to handle handwritten documents and complex layouts, along with the promotion of business automation through RPA integration, are major benefits for companies wishing to advance DX (Digital Transformation).
However, implementation involves initial and running costs, and seeking perfect accuracy requires human visual checks and improved annotation precision.
Continuing to rely on conventional manual document processing means spending time on tasks that should be streamlined. For companies operating character recognition with conventional systems, implementing AI-OCR is recommended.
Those with concerns about implementation costs or annotation technology should also consider requesting help from a specialized company.
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