脱机自由手写英文单词的识别

介绍了一个基于隐马尔科夫模型的、采用模糊分割方式的脱机手写英文单词识别系统。该系统由图像预处理、特征提取、基于HMM的训练和识别四个模块组成。图像预处理中包括二值化、平滑去噪、倾斜校正和参考线提取。然后通过宽度不固定的滑动窗提取特征，前两组特征是整体形状和象素分布特征，另外又引入了Sobel梯度特征。HMM模型采用嵌入式的Baum-Welch算法训练，这种训练方式无需分割单词。最后用Viterbi算法识别。对字典中的每个单词，采用字母模型线性连接成单词模型。

Off-line cursive English word recognition

Our off-line cursive English word recognition system was based on HMM(Hidden Markov Models) and blur segmentation. It was composed of four modules: image preprocess, feature extraction, HMM-based training and word recognition. The image preprocess consisted of four components: binarization, smoothing and noise removing, incline correction, and reference line detection. Through an unfixed-width sliding window, the features of shape, pixel distribution and Sobel gradient were extracted. HMMs were trained by embedded Baum-Welch algorithm without the segmentation of words. After concatenating letter models to word models for every word in the dictionary, the handwritten English word could be recognized by Viterbi algorithm.