Abstract
Recognizing financial identifiers from variable or degraded optical inputs can be challenging, as conventional deterministic scanners may fail and general-purpose optical character recognition systems may lack semantic logic. A system can employ a cascaded inference pipeline that first assesses the visual entropy of an optical input. Based on this assessment, the input can be routed to either a fast, deterministic decoder for low-entropy signals or a probabilistic inference engine for high-entropy signals. The probabilistic engine could generate multiple candidate identifiers and use a reasoning model, constrained by financial domain logic, to evaluate a plausible candidate. A validation layer can then apply checks, such as checksum algorithms, to a selected candidate. This approach may improve the extraction of financial data from varied optical conditions by balancing processing speed with semantic accuracy.
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This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ngare, Jack, "Entropy-Gated Probabilistic Inference for Optical Financial Identifier Recognition", Technical Disclosure Commons, (March 11, 2026)
https://www.tdcommons.org/dpubs_series/9495