What are the best OCR software of 2020?

In 2020, the best OCR software included Tesseract, ABBYY FineReader, and cloud-based solutions like Google Cloud Vision OCR and Amazon Textract. These tools were widely adopted by developers due to their accuracy, flexibility, and integration capabilities. Tesseract, an open-source OCR engine maintained by Google, stood out for its customizability and support for multiple languages. ABBYY FineReader offered high precision in handling complex layouts, while cloud APIs like Google’s and Amazon’s provided scalable, machine learning-driven text extraction. Each tool catered to different use cases, from cost-sensitive projects to enterprise-grade document processing.

Tesseract was a popular choice for developers due to its open-source nature and compatibility with Python, Java, and other languages via wrappers. Its LSTM-based engine (introduced in version 4.0) improved accuracy for unstructured text, though it required tuning for specific fonts or layouts. ABBYY FineReader 15, released in 2020, excelled in processing multi-column documents, tables, and low-quality scans, making it ideal for legal or financial applications. Cloud services like Google Cloud Vision OCR and Amazon Textract offered REST APIs for seamless integration into web apps. For example, Amazon Textract specialized in extracting structured data from forms or invoices, while Google’s API supported handwriting recognition. These services reduced infrastructure overhead but incurred costs based on API calls.

Developers prioritized factors like integration ease, language support, and cost. Tesseract was free but required manual setup, while ABBYY’s SDKs simplified deployment at a higher price. Cloud APIs were pay-as-you-go, suitable for scalable projects but potentially costly for high-volume use. For multi-language projects, Tesseract supported over 100 languages, whereas ABBYY and cloud providers covered fewer but included advanced features like layout analysis. A developer building a mobile app might choose Tesseract for offline use, while an AWS-based project could leverage Textract for structured data extraction. The decision often hinged on balancing accuracy, budget, and technical requirements specific to the application.