Artificial intelligence-based machine readable symbol reader

Abstract

Systems and methods for establishing optimal reading conditions for a machine-readable symbol reader. A machine-readable symbol reader may selectively control reading conditions including lighting conditions (e.g., illumination pattern), focus, decoder library parameters (e.g., exposure time, gain), etc. Deep learning and optimization algorithms (e.g., greedy search algorithms) are used to autonomously learn an optimal set of reading parameters to be used for the reader in a particular application. A deep learning network (e.g., a convolutional neural network) may be used to locate machine-readable symbols in images captured by the reader, and greedy search algorithms may be used to determine a reading distance parameter and one or more illumination parameters during an autonomous learning phase of the reader. The machine-readable symbol reader may be configured with the autonomously learned reading parameters, which enables the machine-readable symbol reader to accurately and quickly decode machine-readable symbols (e.g., direct part marking (DPM) symbols).

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