RIA: Handwritten Word Recognition Using First and Second Order Hidden Markov Model

隐马尔可夫模型（HMM）是一个双随机过程， 不可观测的潜在随机过程，即，国 隐藏，但只能通过另一组随机观察 产生可观察的符号序列的过程。 在这 工作中，手写体脚本识别问题建模在 HMM框架。 对于英文文本，这是目前的重点 研究表明，各州是用字母表中的字母来识别的， 并通过实验研究产生了最佳符号。 15个特征（一些旧的，一些新的）用于此任务。 一阶和二阶隐马尔可夫模型都用于 识别任务 利用现有的统计知识， 英语，如密码学等，的计算方案， 对于一阶模型，模型参数被极大地简化。 扩展这些结果，并通过详尽的字典搜索， 计算二阶模型的概率。 维特比 算法用于识别单个最优状态序列， 也就是说，组成单词的字母序列。 的修改 还 研究过了 最后，第一和第二个实验结果 提供了订单模型。

A hidden Markov model (HMM) is a doubly stochastic process with an underlying stochastic process that is not observable, i.e., states are hidden, but can only be observed through another set of stochastic processes that produce the observable sequence of symbols. In this work, the handwritten script recognition problem is modeled in the framework of HMM. For English text, which is the focus of the present research, the states are identified with the letters of the alphabet, and the optimum symbols are generated by means of experimental study. Fifteen features (some old, some new) are used for this task. Both the first and second order hidden Markov models are used for the recognition task. Using the existing statistical knowledge of the English language as in Cryptography etc., the calculation scheme of the model parameters are immensely simplified for the first order model. Extending these results and through an exhaustive dictionary search, probabilities of the second order model are calculated. Viterbi algorithm is used to recognize the single best optimal state sequence, i.e., sequence of letters consisting the word. The modification of the recognition algorithm to accommodate context information is also researched. Finally, experimental results for the first and second order models are provided.