In this article, we explain exactly how to improve OCR accuracy and understand its importance. We also cover how to calculate it to optimize your OCR processes. Read on to learn more.
OCR (Optical Character Recognition) accuracy refers to the precision with which OCR software can convert different types of documents, such as scanned paper documents, PDF files, or images taken by a digital camera, into editable and searchable data. High accuracy OCR ensures that the extracted text closely matches the original content, minimizing errors and the need for manual corrections.
Example: When using Adobe Acrobat's OCR feature on a scanned contract, the software achieved a 98% accuracy rate. This implies that it correctly identified almost all the text including complex legal terminology. This high accuracy drastically reduced the time required for manual proofreading and corrections.
Understanding the accuracy of optical character recognition is important for several reasons, some of the most common include:
High accuracy OCR reduces the time and effort required to manually correct errors, leading to more efficient data processing.
Minimizing errors through accurate OCR reduces the cost associated with manual corrections and data entry.
Accurate OCR ensures that the extracted data is reliable, maintaining the integrity of information used for decision-making and analysis.
Accurate text recognition enhances the searchability of documents, making it easier to find and retrieve specific information.
Improving OCR accuracy is essential for processing documents. Here's how to do it:
Ensure that the images are clear and of high resolution. Blurry or low-resolution images can significantly reduce OCR accuracy.
Example: A law firm, LegalEase, enhances the quality of their scanned documents by using high-resolution scanners, resulting in more accurate text recognition for their legal records.
Make sure the text is correctly oriented. Skewed or tilted text should be corrected to be horizontal.
Example: TechCorp uses image processing software to correct the orientation of scanned engineering drawings, improving OCR accuracy.
Remove any background noise or artifacts. This can be achieved using filters to enhance the contrast between text and background.
Example: MediScan applies noise reduction filters to medical forms, ensuring clearer text extraction for patient records.
Convert images to black and white (binarization) to improve the distinction between text and background.
Example: RetailerShop uses binarization techniques on scanned receipts, which enhances accuracy for their accounting department.
Ensure that documents are scanned at a high resolution (300 DPI or higher). Higher resolution scans provide more detail, which can significantly improve accuracy.
Example: FinTech Solutions scans financial documents at 600 DPI, resulting in higher accuracy for their data analysis.
Use advanced OCR software that supports machine learning and AI. Some well-regarded OCR tools include Lido, Google Cloud Vision, and Tesseract.
Example: HealthCare Inc. adopts Lido for processing patient files, benefiting from its advanced AI capabilities.
Use standardized document layouts where possible. Consistent layouts make it easier for OCR software to predict and accurately recognize text.
Example: EduBooks standardizes the layout of their educational materials, improving accuracy for digital archiving.
Implement a spell checker to identify and correct misrecognized words. This can be particularly useful for documents with a lot of text.
Example: RealEstatePro uses post-processing tools to correct OCR errors in property documents, ensuring data accuracy.
Train the OCR software on a representative sample of your documents. This helps the OCR engine learn specific characteristics of your text, such as custom fonts and uncommon language patterns.
Example: LanguageWorks customizes their OCR software to recognize multiple languages, improving accuracy for multilingual documents.
Keep your OCR software and any related tools up to date. Updates often include improvements in text recognition algorithms and support for new languages and fonts.
Example: eCommerceHub regularly updates their OCR software to leverage the latest advancements in text recognition, maintaining high accuracy for their product catalogs.
Here are some of the most common factors affecting the accuracy of optical character recognition:
The clarity and resolution of the scanned image significantly impact OCR accuracy. Poor quality images with low resolution, noise, or blurriness can lead to incorrect character recognition.
The type and size of the font used in the document influence OCR performance. Standard fonts are more easily recognized by OCR systems, while unusual, decorative, or very small fonts can cause errors.
Complex layouts with multiple columns, tables, or irregular text alignments can confuse OCR software. Simple, well-organized text layouts improve recognition accuracy.
The language and specific characters used in the text affect the accuracy of OCR. OCR systems trained on a specific language or character set perform better on texts in that language or set, while texts with mixed languages or special characters can pose challenges.
Effective preprocessing techniques like binarization, deskewing, and noise reduction enhance OCR accuracy. Proper preprocessing can correct image imperfections and standardize the input for better recognition.
The contrast between the text and its background is crucial. High contrast (e.g., black text on a white background) makes it easier for OCR to differentiate characters, while low contrast (e.g., light grey text on a white background) can reduce accuracy.
Recognizing handwritten text is inherently more challenging for OCR systems than printed text. Variations in handwriting style and quality can lead to significant recognition errors.
Use our 4 step process to calculate the accuracy of OCR:
Establish a correct version of the text to be used as a reference. This ground truth is critical for comparing the OCR output and measuring accuracy.
Example: For instance, if we have a scanned page of a book from HarperCollins with exactly 2,000 characters, this text serves as the ground truth.
Use OCR software to process the scanned image and generate a text output. This output will be compared against the ground truth to assess accuracy.
Example: After running the OCR on a Canon scanner using Tesseract OCR, the software produces an output text of the scanned HarperCollins page.
Identify and count the errors by comparing the OCR output with the ground truth. Errors can be substitutions, deletions, or insertions of characters.
Example: Comparing the OCR output to the HarperCollins page, we find that there are 30 character errors where characters were either substituted, deleted, or incorrectly inserted.
Use the formula:
This percentage represents the OCR system's accuracy.
Example: If the OCR output correctly recognizes 1,970 characters out of 2,000, the OCR accuracy rate would be:
This means the Tesseract OCR accurately recognized 98.5% of the characters in the HarperCollins page.
We hope you now have a better understanding of how to improve OCR accuracy and how to calculate the accuracy of OCR processes. If you enjoyed this article, you might also like our article on what is OCR technology or our article on OCR zone.