A segmentation-free strategy based on hidden Markov models (HMMs) is presented for offline recognition of unconstrained Chinese handwriting. As the first step, handwritten textlines are converted to observation sequence by sliding windows and character segmentation stage is avoided prior to recognition. Following that, embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best character string maximizing the a posteriori is located through Viterbi algorithm. Experiments are conducted on the HIT-MW database written by more than 780 writers. The results show: First, our baseline recognizer outperforms one segmentation-based OCR product with 35% relative improvement; second, more discriminative feature and compact representation, and state-tying technique to alleviate the data sparsity can enhance the recognizer with high confidence. The final recognizer has improved the performance by 10.77% than the baseline system.