In this article, we will explore exactly what AI-powered OCR is. We also share our 7 step process for AI-based OCR. Read on to learn more.
AI OCR (Artificial Intelligence Optical Character Recognition) is a technology that combines AI algorithms with OCR to enhance the accuracy and efficiency of extracting text from images and documents. It enables machines to interpret, analyze, and convert printed or handwritten text into machine-readable data.
Example: AI OCR is used in the ScanSnap iX1500 document scanner to quickly digitize stacks of paper documents into editable and searchable PDFs. This technology is also employed in mobile banking apps like Chase Mobile to accurately capture and process check information for deposits without manual data entry.
OCR powered by AI is important for a number of reasons, some of the most common reasons include:
AI-powered OCR enhances text recognition accuracy, especially in low-quality or handwritten documents. By learning from numerous examples, AI adapts to different fonts and styles, reducing errors and enhancing data extraction reliability.
AI-integrated OCR automates data entry, turning hours of manual transcription into seconds of automated scanning. This speeds up workflows and frees up resources for tasks needing human input.
AI with OCR reduces manual data entry costs and errors, which lead to costly corrections. This efficiency lets businesses allocate budgets more effectively.
AI-enhanced OCR makes data more accessible by turning scanned documents into editable, searchable formats. This aids in creating digital archives that are easily searchable and accessible which improves data retrieval.
AI OCR systems are highly scalable and capable of handling increasing volumes of documents without the need for additional human resources. This makes it ideal for organizations that experience fluctuating or growing amounts of data entry needs.
AI OCR improves document security by digitizing sensitive information in secure, encrypted formats. This lowers data loss or theft risks and supports robust compliance with data protection regulations.
Use our 7 step AI-powered OCR technology process to effectively digitize and manage your textual data. Simply follow the steps below:
Optical Character Recognition technology converts images of text into machine-readable text data. Choose an OCR engine based on accuracy, language support, and integration capabilities.
Example: For processing sales invoices from an image format, Tesseract OCR can accurately convert texts and numbers, recognizing product names like "Espresso Machine" and amounts like "1,250 units."
Preprocessing improves the OCR's accuracy by enhancing image quality. This involves adjusting brightness, contrast, and converting images to grayscale.
Example: Before scanning a purchase order, adjust the contrast to make the product names like "French Press" and quantities like "350 units" more distinguishable on a light background.
Text localization segments the image to identify where the text is located. This step is crucial for images containing both text and non-text elements.
Example: In an inventory report, localize sections listing items like "Coffee Grinder" alongside their prices such as "$59.99" to focus OCR only on relevant areas.
This is the core of OCR where the actual text conversion happens. The OCR engine analyzes each localized text block and converts them into digital text.
Example: Convert the image of a label that says "Total: 200 units of Smartphone" into editable text format for further processing in a stock management system.
Correct errors in OCR output to enhance accuracy. This might involve using spell checkers or custom dictionaries.
Example: After converting receipts, a post-processing script corrects misreadings such as "Lapptop" to "Laptop" for an inventory list that includes "120 units priced at $800 each."
Integrate OCR results with databases or other applications for storage, retrieval, and further analysis.
Example: Upload the recognized text containing "Tablet 30 units at $299 each" into a sales database to update inventory and sales records automatically.
Use feedback to train the OCR system continuously, improving its recognition capabilities over time.
Example: Retrain the OCR model using outputs like "Espresso Machine sold 150 units" that were initially misread, to reduce future errors in similar transactions.
DataDoc Solutions Inc. is a leading provider of data management and digital transformation services, specializing in efficient document processing and management. Here's how they implemented our simple AI-powered OCR process.
DataDoc Solutions evaluates several OCR technologies and selects Google Cloud Vision for its superior accuracy in recognizing diverse fonts and document layouts. The selection process involved testing OCR accuracy on a sample of 200 mixed-format documents, including invoices and contracts.
Implement a preprocessing protocol where each incoming document image undergoes automatic adjustments for optimal brightness and contrast. This step was critical for processing 500 daily transaction documents from clients, enhancing the clarity of faded text and handwritten notes.
Develop a custom script to detect and isolate text regions in complex document layouts, such as financial reports and technical diagrams. This script successfully localized text in 300 multi-page reports per week, focusing OCR efforts on textual content and ignoring irrelevant graphic elements.
Apply the OCR engine to convert localized text blocks into digital text across all incoming documents. This conversion process is crucial for the weekly processing of over 1,000 client emails and digital forms, ensuring accurate data capture of essential information like dates, amounts, and client details.
Integrate post-processing tools to correct common OCR errors. Implement a spell-checker and a custom industry-specific dictionary to refine the OCR output of technical terms that are frequently misinterpreted. This adjustment improved the text accuracy in over 750 weekly reports.
Automate the integration of corrected OCR data into DataDoc’s central database system. This automation supports real-time data updates and retrieval, crucial for maintaining accurate and current client records across 400 active accounts.
Establish a routine for continually training the OCR model using manually corrected outputs as new training data. This initiative focuses on adapting the OCR system to newly encountered document types and errors, enhancing its recognition capabilities and processing efficiency for future documents.
We hope that you now have a better understanding of what AI powered OCR is and how to use our simple 7 Step AI-Based OCR process. If you enjoyed this article, you might also like our article on OCR in document processing or our article on data pulling.