Probabilistic Estimation and Error Bounds in AI-Based OCR Systems for Enterprise Finance

Optical Character Recognition (OCR) has become essential for automating document workflows in enterprise finance, handling tasks such as invoice processing, tax extraction, and compliance reporting. While recent improvements have been made on AI-based OCR systems, few of the current models output calibrated confidence estimates or measure uncertainty, and as a result, they pose critical risks in high-stakes financial applications. These systems normally provide deterministic outputs with no facilities for measuring prediction accuracy, which denies transparency and trust in computerized financial decision-making. This study introduces a new probabilistic OCR paradigm that integrates uncertainty estimation and error-bound modeling into the AI pipeline. Built upon the LayoutLM transformer architecture, the framework employs Monte Carlo Dropout during inference to generate multiple predictions per input, enabling the computation of predictive entropy and confidence intervals for both categorical and numerical fields. The methodology includes preprocessing scanned financial documents from the SROIE v2 dataset, text region segmentation, supervised label alignment, and key-value pairing for structured extraction. The implementation uses PyTorch and HuggingFace Transformers, supported by statistical post-processing to flag uncertain outputs and reduce operational risk. Evaluation results demonstrate high reliability, with the proposed system achieving 99.13% accuracy, a mean confidence interval width of ±1.22 for financial fields, and an expected calibration error of just 2.9%. Approximately 12.4% of predictions are flagged for manual review, effectively balancing automation with oversight. By combining layout-aware modeling with principled uncertainty quantification, the system enhances reliability, explainability, and risk-awareness in enterprise finance, making it a strong candidate for trustworthy financial document automation.