Comparative Analysis of OCR Engine Performance

This white paper presents a comparative analysis of Optical Character Recognition (OCR) engines, focusing on their performance metrics and effectiveness in various document processing scenarios. The analysis utilizes a dataset derived from the Document UnderstanDing of Everything (DUDE) challenge, which includes a diverse range of document types to evaluate the OCR solutions. The paper outlines the evaluation methodology, emphasizing the use of Character Error Rate (CER) as a key performance indicator. It categorizes the OCR engines into commercial, open-source, and multimodal large language model-based solutions, detailing their respective strengths and weaknesses. The results indicate that commercial engines, particularly PLANET AI's IDA, outperform open-source counterparts significantly, achieving the lowest CER. The findings underscore the importance of data quality in AI applications and highlight the varying capabilities of different OCR technologies in handling complex document layouts and text recognition tasks. The paper concludes with insights into the implications of these findings for future OCR technology developments.