Surface electromyography (sEMG) offers a promising path toward private and wearable text input, but accurate typing transcription remains challenging due to strong cross-user and cross-session variability. We propose a beam-aware sEMG typing framework that connects an improved Connectionist Temporal Classification (CTC)-based frontend with a large language model (LLM) corrector for exact recovery of the typed sequence. As a standalone sEMG decoder, our CNN-Conformer frontend achieves the best reported performance among the compared published emg2qwerty systems under the full-session frontend protocol, reaching 19.94% generic character error rate (CER) and 3.43% personalized CER. For LLM correction, we use a separate windowed hypothesis-export protocol, where the frontend’s top-10 CTC hypotheses are provided to a fine-tuned LLM together with a rank-1 baseline. Under this within-protocol comparison, the LLM reduces session-level CER from 22.53% to 19.65% in the generic setting and from 3.41% to 2.01% after personalization. A matched hybrid evaluation further confirms that the LLM also improves rank-1 outputs obtained from full-session decoding. The top-10 oracle results show that the beam contains recoverable information beyond the rank-1 output. These results demonstrate that a stronger sEMG frontend and hypothesis-aware LLM correction can work together effectively for exact sEMG typing transcription.