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Many people may be considering implementing non-standard form OCR but hesitate due to concerns about accuracy. OCR (Optical Character Recognition) is a highly useful technology that converts paper documents into digital data. However, when dealing with non-standard forms that have flexible layouts and inconsistent formats, accuracy issues tend to become more pronounced.
Even if OCR is introduced, low recognition accuracy may lead to an increased need for manual corrections. As a result, the expected improvements in operational efficiency and cost reduction may not be fully realized. Therefore, in this article, we introduce seven specific methods to dramatically enhance the accuracy of non-standard form OCR.
By applying these techniques, you can significantly improve OCR accuracy, streamline operations, and reduce costs. We encourage you to read through to the end and apply these insights to your business.
This article provides a comprehensive explanation, starting with the fundamentals of non-standard form OCR, followed by specific techniques for improving accuracy, and finally exploring the future of OCR through the utilization of the latest technologies.
What Is Non-Standard Form OCR?
If you want to learn more about AI OCR, be sure to check out this article first.
Related article: Innovative Applications Of Generative AI OCR And Five Key Methods
Basic Knowledge of OCR: Mechanism and Types
OCR (Optical Character Recognition) is a technology that extracts text information from image data. Specifically, it enables computers to read text from scanned or photographed paper documents. OCR is widely used for digitizing various types of documents, significantly contributing to operational efficiency and cost reduction. There are four main types of OCR:
- Traditional OCR (Traditional Pattern Recognition): This method recognizes text by matching image data with predefined character templates. It delivers high accuracy for standardized fonts and layouts but lacks flexibility.
- Optical Mark Recognition (OMR): This method identifies specific patterns, such as checkboxes and mark sheets. It is widely used for surveys and test scoring.
- Intelligent Character Recognition (ICR): This method recognizes handwritten characters, which do not follow a fixed pattern. It is suitable for recognizing handwritten forms and signatures.
- Barcode Recognition: This method reads symbols such as barcodes and QR codes. It is commonly used for product and inventory management.
Challenges and Solutions of Non-Standard Form OCR
Non-standard forms refer to documents that do not follow a fixed format. Examples include invoices and purchase orders, which vary in layout depending on the company. Non-standard form OCR is a technology that extracts text information from such flexible layouts, but traditional OCR often struggles with accuracy. Conventional OCR technologies alone are often insufficient to handle the wide variety of formats and layouts.
To address this challenge, advanced OCR technologies utilizing AI and machine learning have emerged. AI OCR learns text characteristics from large datasets, enabling high-accuracy recognition even for non-standard forms. Machine learning algorithms automatically identify and learn patterns, allowing for greater adaptability to diverse layouts and formats.
7 Secrets to Improving the Accuracy of Non-Standard Form OCR
1. Use High-Quality Images: The Importance of Scanners and Resolution
The accuracy of OCR is greatly influenced by the quality of the input images. Using high-quality images improves OCR recognition accuracy. Specifically, it is important to use a high-performance scanner and scan at an appropriate resolution.
- Choosing a Scanner: Select a scanner by considering factors such as resolution, scanning speed, and supported file formats. A high-performance scanner provides clearer images, contributing to improved OCR accuracy.
- Setting the Resolution: A resolution of at least 300 dpi is generally recommended. Higher resolution results in clearer character recognition and reduces the risk of misinterpretation. However, excessively high resolution increases file size and may slow down processing speed, so it is necessary to find a balance.
2. Image Preprocessing: Noise Removal and Contrast Adjustment
Scanned images may contain noise and dirt. These noise factors can reduce the accuracy of OCR, so it is important to remove them through preprocessing.
- Noise Removal: Using image editing software or dedicated preprocessing tools, noise and dirt within the image are removed. This makes it easier for OCR to recognize characters accurately.
- Contrast Adjustment: By adjusting the contrast between the characters and the background, the characters stand out more clearly, improving recognition accuracy. In particular, if there are spots or shadows on the background, increasing the contrast can improve the visibility of the characters.Choosing the Right OCR Software: Leveraging AI OCR
3. There Are Various Types of OCR Software
For non-standard forms, it is important to select the appropriate software, such as AI OCR, tailored to the type and purpose of the form, rather than using a simple OCR software.
- AI OCR: AI OCR can achieve high-accuracy character recognition even for non-standard forms, as it learns the characteristics of characters from large volumes of data. By leveraging machine learning algorithms, it can flexibly handle complex layouts and various formats.
- Cloud-based OCR: Cloud-based OCR services are also an option. Since they can be accessed via the internet, they help reduce implementation costs and offer good scalability. However, it is important to check the reliability of the service from the perspective of security and data privacy before using it.
4. Utilizing Layout Recognition Features: Structuring Text
Modern OCR software includes layout recognition features. By utilizing this feature, the structure of the text can be recognized, enabling more accurate data extraction.
- Extracting Tabular Data:
By using the layout recognition feature, tabular data can be extracted accurately. This allows the data in the table to be utilized as digital data while maintaining its positional relationships and structure. - Linking Item Names and Values:
By automatically linking item names and their values, the effort required for data entry is significantly reduced. This maintains data integrity and ensures smooth processing of subsequent data.
5. Dictionary Registration: Supporting Technical Terms
Some OCR software includes a dictionary registration feature. By registering technical terms or industry-specific terminology in the dictionary, the recognition accuracy can be improved.
- Preventing Misrecognition:
With dictionary registration, OCR software can accurately recognize technical terms. This reduces misrecognition and improves data accuracy. - Improving Recognition Rate:
Registering technical terms in the dictionary improves the overall recognition rate of the OCR software. This is particularly effective for forms that use many industry-specific terms.
6. Creating Templates: Optimization for Form Types
When repeatedly processing the same type of form with OCR, creating templates leads to improved accuracy and reduced processing time.
- Fixing Item Positions:
By using templates, the positions of each item can be fixed. This makes it easier for OCR software to recognize the text and reduces misrecognition. - Reducing Processing Time:
Using templates significantly reduces OCR processing time. With a fixed layout, the software can efficiently extract data, improving overall processing speed.
7. Human Review: The Importance of Final Confirmation
After OCR processing, it is crucial to perform a human check. While OCR software is highly accurate, it is not perfect, and there is always a possibility of misrecognition.
- Correcting Misrecognition:
By performing a human check, any misrecognition made by the OCR software can be corrected. This improves the accuracy of the data. - Improving Data Accuracy:
Through final confirmation, the accuracy of the data is further enhanced. For important business data, this review process is essential to ensure reliability.
The Future of Unconventional Form OCR: Evolution Through AI Technology
With the evolution of AI technology, the accuracy of OCR is expected to continue improving in the future. In particular, deep learning technology has made a significant contribution to improving the accuracy of OCR.
- Advancements in Handwritten Character Recognition:
Deep learning has dramatically improved the accuracy of handwritten character recognition. As a result, the digitization of handwritten forms and signatures can now be done more accurately and efficiently. - Enhanced Multilingual Support:
OCR software using deep learning has strengthened multilingual support. This makes it easier for globally expanding businesses to process multilingual documents, which is expected to improve the efficiency of international operations.
Conclusion: Maximizing the Use of Unconventional Form OCR
Unconventional form OCR is a powerful tool for achieving business efficiency and cost reduction. By implementing the 7 tips for improving accuracy, you can maximize the effectiveness of OCR. With the evolution of AI technology, OCR will continue to evolve, and it is expected to offer even higher accuracy and flexibility in the future. By adopting these strategies, accelerate the digitalization of your business.