NeuroInk: Retinomorphic Spiking Sequence Modeling for Handwritten Text Recognition

Static-input Spiking Neural Networks (SNNs) commonly create temporal dynamics by repeating the same image over multiple timesteps. For handwritten text recognition, however, this convention is input-redundant: the image contains no native event stream, while recognition depends on faint stroke fragments, degraded ink boundaries, and monotonic left-to-right alignment. We introduce NeuroInk, a retinomorphic spike-oriented recognizer that converts static handwriting into a low-timestep sequence of temporally ordered ink evidence. Instead of replaying identical frames, NeuroInk performs temporal ink transduction, decomposing each text-line image into coarse stroke-mass support and fine stroke-boundary evidence. The resulting evidence sequence is injected as time-varying currents into an adaptive spiking visual hierarchy, where high-frequency enhancement and mid-level detail reinjection are used to preserve fine ink structures during spiking propagation. For CTC decoding, two-dimensional spiking maps are read out as width-wise columnar sequences and processed by CTC-aligned association circuits that combine local horizontal context, global association, and selective trace memory. On the LAM benchmark, NeuroInk achieves 2.4\% CER on the validation split and 2.6\% CER on the test split using only two timesteps and greedy CTC decoding, without an external language model or lexicon. These results support the view that static-input SNNs sequence recognition can benefit from co-designing sensory temporalization, spiking visual abstraction, and monotonic sequence association.