一种新的隐马尔可夫模型及其在手绘图形识别中的应用

提出了一种新的隐马尔可夫模型——自适应隐马尔可夫模型（AHMM）．与传统的开环HMM相区别，AHMM是一种用于识别的带反馈机制的闭环HMM．AHMM采用带有压缩率调整因子的特征压缩算法，首先对待识别的特征序列进行较高压缩率的压缩，然后将压缩得到的特征序列送入HMM识别器进行识别．根据对识别效果满意度的判决，确定是否需要调整压缩率因子以获得较长的特征序列，并重新送入HMM识别器进行识别．将该文提出的AHMM用于联机手绘图形的识别，实验表明，AHMM方法与传统的HMM方法相比，识别率和识别速度均有显著提高．

A Novel Hidden Markov Model and Its Application to Recognize Hand-Drawn Graphic Symbol

A novel Hidden Markov Model (HMM) structure for recognizing feature sequence, an adaptive HMM (AHMM) structure, is presented. Distinguished from traditional forward HMM, The AHMM is a closed loop feedback HMM recognition structure. Firstly, a feature compression method, which has a changeable factor used to adjust the feature compression ratio, is applied to compress feature vectors with a high compression ratio. Next, compressed feature sequence is supplied to HMM recognition system. If recognition result is not satisfaction, the compression ratio factor is changed and compress feature vectors are constructed again to obtain longer feature sequence. New feature sequence is supplied to HMM recognition system until a satisfactory recognition is obtained. Superior performance over traditional HMM recognition system is testified by sets of experiment of free form hand-drawn graphic symbol recognition based on AHMM, in which single-band integral algorithm is used to compress feature vectors adaptively according to geometry feature of input graphics symbol.