Abstract

The automated digitization of handwritten electoral results is critical for ensuring transparency and speed in democratic processes. While recurrent sequence-to-sequence models (e.g., CRNN+CTC) achieve high accuracy, they inherently violate the strict latency constraints of high-throughput administrative environments. Conversely, standard lightweight CNNs exhibit suboptimal performance on the long-tail distribution of high-cardinality scenarios. To bridge this gap, this study reformulates sequence recognition into a latency-bound classification task. We propose a specialized Handwritten Digit Sequence Recognition (HDSR) framework for the Mexican Preliminary Election Results Program (PREP) based on a modified ResNet-18 architecture. The methodology introduces an asymmetric stride designed explicitly to preserve the 1:3 horizontal feature resolution of electoral tally sheets, integrating a lightweight Convolutional Block Attention Module (CBAM) in deep stages to refine classification across 1001 possible sequences. Leveraging a megadiverse dataset of 3.77 million real-world images, the model was trained using AdamW and label smoothing to mitigate human-induced label noise. Results demonstrate a global accuracy of 97.82% and a significant improvement in Macro-Precision (0.8878) for rare sequences. With an inference latency of 9.1 ms on standard CPU hardware, the proposed solution offers a scalable, high-confidence alternative that prioritizes spatial preservation and fail-controlled deployment.

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