IRONOCRの使い方 なぜIronOCRがLLMよりもOCRに最適なのか Kannapat Udonpant 更新日:7月 28, 2025 Download IronOCR NuGet Download テキストの検索と置換 テキストと画像のスタンプ Start Free Trial Copy for LLMs Copy for LLMs Copy page as Markdown for LLMs Open in ChatGPT Ask ChatGPT about this page Open in Gemini Ask Gemini about this page Open in Grok Ask Grok about this page Open in Perplexity Ask Perplexity about this page Share Share on Facebook Share on X (Twitter) Share on LinkedIn Copy URL Email article Introduction With the rise of Large Language Models (LLMs), many companies have attempted to use them for Optical Character Recognition (OCR) and document parsing. However, LLMs often fall short in this area due to their tendency to "hallucinate"—generating incorrect or fabricated text rather than accurately extracting information from documents. In contrast, dedicated OCR solutions like IronOCR provide superior accuracy, reliability, and efficiency when working with PDFs and other document formats. In this article, we will explore the weaknesses of LLMs in OCR and compare them with IronOCR to demonstrate why specialized tools are the better choice. The Limitations of LLMs for OCR 1. Hallucination and Inaccuracy LLMs are designed to generate text based on probabilities, which makes them prone to hallucinations—creating content that was never present in the source document. This is a significant issue when performing OCR, as even minor errors can result in lost or misinterpreted data. 2. Lack of Structured Output Unlike dedicated OCR tools, LLMs struggle to extract structured data from documents, making them unsuitable for parsing invoices, forms, and other structured documents accurately. 3. Computational Overhead Running OCR with an LLM typically requires substantial computational resources, as the models must process large amounts of text data before generating meaningful output. This results in higher costs and slower performance compared to optimized OCR solutions. 4. Inconsistent Performance Across Document Types LLMs may work reasonably well for simple text documents but often struggle with scanned PDFs, handwritten text, or documents with complex formatting. Their performance varies widely depending on the document type, making them unreliable for enterprise applications. Asking an AI (e.g., Google Gemini) to Perform OCR Some users attempt to perform OCR by uploading an image to an AI chatbot like Google Gemini and requesting it to extract the text. While this might work in certain cases, it comes with notable drawbacks: Limited control: AI models often process images in a black-box manner, meaning users have little control over how the text is extracted or formatted. Inconsistent results: The accuracy of AI OCR depends heavily on the model's training data and can be unreliable for complex or handwritten documents. Privacy concerns: Uploading sensitive documents to an AI service raises security and confidentiality risks. Limited integration: Unlike dedicated OCR solutions, AI chatbots do not provide easy ways to integrate OCR into existing workflows. Why IronOCR is the Better Solution IronOCR is a purpose-built OCR library for .NET that delivers high accuracy and reliability. Here’s why it outperforms LLMs for OCR tasks: 1. High Accuracy and Reliability IronOCR is optimized for extracting text from images and PDFs with precision. Unlike LLMs, it does not generate hallucinated text but rather extracts exactly what is present in the document. 2. Supports Complex and Structured Documents IronOCR can accurately process structured documents such as invoices, contracts, and forms, making it ideal for businesses that rely on precise data extraction. 3. Efficient and Cost-Effective Unlike LLM-based OCR, which requires significant computational power, IronOCR is lightweight and optimized for speed. This makes it a cost-effective solution that does not require expensive cloud-based models. 4. Better Handling of Noisy and Low-Quality Scans IronOCR includes built-in noise reduction and image enhancement capabilities, allowing it to extract text from noisy, low-resolution, or distorted scans more effectively than LLMs. IronOCR: A Leading OCR Library IronOCR is a robust OCR library designed specifically for .NET developers, offering a seamless and accurate way to extract text from scanned documents, images, and PDFs. Unlike general-purpose machine learning models, IronOCR is engineered with a focus on precision, efficiency, and ease of integration into .NET applications. It supports advanced OCR capabilities such as multi-language recognition, handwriting detection, and PDF text extraction, making it a go-to solution for developers who need a reliable OCR tool. Key Features of IronOCR IronOCR offers a range of features that make it an industry-leading OCR solution: Multi-Language Support: Recognizes and extracts text from documents in multiple languages. Advanced Document Capabilities: Capable of handling advanced specific documents such as passports and license plates. PDF and Image OCR: Works with scanned PDFs, TIFFs, JPEGs, and other image formats. Searchable PDFs: Converts scanned documents into fully searchable PDFs. Barcode and QR Code Recognition: Detects and extracts data from barcodes and QR codes. Performance Comparison: LLM vs. IronOCR To illustrate the difference, let’s compare the results of extracting text from a scanned PDF invoice using an LLM and IronOCR. For this example, I will run the following image through both IronOCR and an LLM: IronOCR Code Example: using IronOcr; class Program { static void Main(string[] args) { // Specify the path to the image file string imagePath = "example.png"; // Initialize the IronTesseract OCR engine var Ocr = new IronTesseract(); // Create an OCR image input from the specified image path using var imageInput = new OcrInput(imagePath); // Perform OCR to read text from the image input OcrResult result = Ocr.Read(imageInput); // Output the recognized text to the console Console.WriteLine(result.Text); } } using IronOcr; class Program { static void Main(string[] args) { // Specify the path to the image file string imagePath = "example.png"; // Initialize the IronTesseract OCR engine var Ocr = new IronTesseract(); // Create an OCR image input from the specified image path using var imageInput = new OcrInput(imagePath); // Perform OCR to read text from the image input OcrResult result = Ocr.Read(imageInput); // Output the recognized text to the console Console.WriteLine(result.Text); } } Imports IronOcr Friend Class Program Shared Sub Main(ByVal args() As String) ' Specify the path to the image file Dim imagePath As String = "example.png" ' Initialize the IronTesseract OCR engine Dim Ocr = New IronTesseract() ' Create an OCR image input from the specified image path Dim imageInput = New OcrInput(imagePath) ' Perform OCR to read text from the image input Dim result As OcrResult = Ocr.Read(imageInput) ' Output the recognized text to the console Console.WriteLine(result.Text) End Sub End Class $vbLabelText $csharpLabel Output Explanation This code example uses IronTesseract to extract text from an image file example.png. It initializes the IronTesseract OCR engine and creates an OcrImageInput object to encapsulate the image. The Read method of IronTesseract performs OCR on the image input, and the recognized text is printed to the console. The use of the using statement ensures that resources are properly managed, making OCR both efficient and straightforward. This demonstrates IronOCR's ability to accurately extract text from images in just a few lines of code. Example: Using an LLM for OCR For this example, we have followed the steps outlined below to have Google’s LLM, Gemini, perform OCR on the same image. Steps for Performing OCR with Google Gemini Open Google Gemini (or another AI chatbot that supports image processing). Upload an image containing text. Ask the AI: "Can you perform OCR on this image?" The AI will generate a response containing the extracted text. Review the output for accuracy. While this method can work, it often struggles with precise text extraction, formatting, and structured document processing. The lack of consistency makes it unreliable for professional applications. Output In this example, the LLM struggled to output anything at all, unlike IronOCR, which was capable of extracting all of the text within our test image on the first attempt. LLMs such as Gemini struggle with simple OCR tasks, either incapable of producing all the text contained within an image or they hallucinate words and end up with an output that has nothing to do with the image itself. Why IronOCR is the Better Solution for Usability One major limitation of AI-powered OCR is that the extracted text is simply presented in a message, making it difficult to use for further processing. With IronOCR, the extracted text can be directly used in .NET applications for automation, search indexing, data processing, and more. This allows developers to seamlessly integrate OCR results into their workflows without manually copying and pasting text from an AI chatbot. Performance Comparison: AI OCR vs. IronOCR Why IronOCR is Better IronOCR provides a superior experience for .NET developers compared to Google Cloud Vision API for several reasons: No External API Calls Google Cloud Vision requires internet access and authentication with an API key. IronOCR runs locally, eliminating latency, security concerns, and dependency on external services. Simpler Setup Google Cloud Vision requires setting up credentials, managing API keys, and handling network requests. IronOCR works with a simple NuGet package (Install-Package IronOcr) and requires no API credentials. Better .NET Integration Google Cloud Vision is a cloud-based solution designed for multiple platforms. IronOCR is built specifically for .NET, providing a more seamless development experience. More Control Over OCR Processing IronOCR allows customization (e.g., filters for noise removal, grayscale conversion, OCR tuning). Google Cloud Vision is a black-box solution with limited configurability. Lower Cost for On-Premises Use Google Cloud Vision charges per request. IronOCR has a one-time perpetual licensing option, which can be more cost-effective for large-scale applications. Conclusion While AI-powered LLM OCR tools such as Google Gemini may offer a quick way to extract text from images, they come with serious limitations, including inaccuracy, inconsistent results, and privacy concerns. If you need a reliable, accurate, and cost-effective OCR solution, IronOCR is the clear winner. Unlike AI OCR, it provides structured and precise text extraction, supports integration into .NET applications, and works efficiently on a variety of document types. Additionally, IronOCR allows developers to use the extracted text for automation and further processing, making it far more practical than AI-generated text in chat messages. For businesses and developers who require dependable OCR performance, IronOCR is the best choice. Try IronOCR today by downloading the free trial, and experience the difference in quality and efficiency firsthand! よくある質問 なぜ専門的なOCRツールはテキスト抽出においてLLMよりも正確なのか? IronOCRのような専門的なOCRツールは、文書から直接高精度でテキストを抽出するように設計されており、LLMが生成する誤ったテキストの「幻覚」を避けます。これにより、抽出されたテキストが元の文書に存在するものと全く同一であることが保証されます。 IronOCRは低品質またはノイズの多いスキャンを効果的に処理できますか? はい、IronOCRはノイズリダクションと画像処理の機能を備えており、ノイズの多い、低解像度、または歪んだ文書スキャンを正確に処理します。 IronOCRを使用することによるLLMベースのOCRとの効率性の利点は何ですか? IronOCRは速度に最適化されており、ローカルで実行されるため、大量の計算リソースや、しばしばLLMベースのOCRソリューションによって必要とされる外部APIコールが不要です。 IronOCRはどのように企業レベルのOCRアプリケーションをサポートしますか? IronOCRはスキャンされたPDFや手書きのテキストを含むさまざまな文書タイプを処理でき、信頼性と精度が求められる企業アプリケーションに適しています。 IronOCRは多言語のテキスト認識をサポートしていますか? はい、IronOCRは多言語認識をサポートし、複数の言語で書かれた文書からテキストを抽出することができ、その多様性を高めます。 どのようにしてIronOCRを既存の.NETアプリケーションに統合できますか? IronOCRは.NETライブラリであり、既存の.NETアプリケーションにシームレスに統合でき、オートメーション、検索インデックス、データ処理などのタスクを実行できます。 IronOCRを使用するのにインターネット接続は必要ですか? いいえ、IronOCRはローカルで動作するため、インターネット接続は不要です。このローカル操作により、外部APIコールが不要となり、待ち時間が減少し、セキュリティが向上します。 IronOCRはどのようにデータプライバシーとセキュリティを確保しますか? IronOCRはデータをローカルで処理し、機密情報が外部サーバーにアップロードされることを防ぎ、データのプライバシーとセキュリティを維持します。 Kannapat Udonpant 今すぐエンジニアリングチームとチャット ソフトウェアエンジニア ソフトウェアエンジニアになる前に、Kannapatは北海道大学で環境資源の博士号を修了しました。博士号を追求する間に、彼はバイオプロダクションエンジニアリング学科の一部である車両ロボティクスラボラトリーのメンバーになりました。2022年には、C#のスキルを活用してIron Softwareのエンジニアリングチームに参加し、IronPDFに注力しています。Kannapatは、IronPDFの多くのコードを執筆している開発者から直接学んでいるため、この仕事を大切にしています。同僚から学びながら、Iron Softwareでの働く社会的側面も楽しんでいます。コードやドキュメントを書いていない時は、KannapatはPS5でゲームをしたり、『The Last of Us』を再視聴したりしていることが多いです。 関連する記事 公開日 9月 29, 2025 IronOCRを使用して.NET OCR SDKを作成する方法 IronOCRの.NET SDKで強力なOCRソリューションを構築。シンプルなAPI、エンタープライズ機能、クロスプラットフォーム対応。 詳しく読む 公開日 9月 29, 2025 IronOCRを使用してC# GitHubプロジェクトにOCRを統合する方法 OCR C# GitHubチュートリアル:IronOCRを使用してGitHubプロジェクトにテキスト認識を実装。コードサンプルとバージョン管理のヒントを含む。 詳しく読む 更新日 9月 4, 2025 私たちが文書処理メモリを98%削減した方法:IronOCRのエンジニアリングブレークスルー IronOCR 2025.9は、TIFF処理メモリを98%削減するストリーミングアーキテクチャを採用し、クラッシュを回避し、企業のワークフローのために速度を向上。 詳しく読む IronOCRで検索可能なPDFの力を解き放つ:ウェビナーの概要IronOCRを使用したスキャン...
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