Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
IEEE Trans Pattern Anal Mach Intell. 2011 Apr;33(4):767-79. doi: 10.1109/TPAMI.2010.141.
This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission probabilities. This paper also presents new techniques to remove slope and slant from handwritten text and to normalize the size of text images with supervised learning methods. Slope correction and size normalization are achieved by classifying local extrema of text contours with Multilayer Perceptrons. Slant is also removed in a nonuniform way by using Artificial Neural Networks. Experiments have been conducted on offline handwritten text lines from the IAM database, and the recognition rates achieved, in comparison to the ones reported in the literature, are among the best for the same task.
本文提出了一种使用混合隐马尔可夫模型（HMM）/人工神经网络（ANN）模型来识别非约束性离线手写文本的方法。光学模型的结构部分采用马尔可夫链建模，而多层感知器则用于估计发射概率。本文还提出了新的技术，用于去除手写文本的斜率和斜度，并使用监督学习方法对文本图像的大小进行归一化。通过使用多层感知器对文本轮廓的局部极值进行分类，实现了斜率校正和大小归一化。通过使用人工神经网络以非均匀的方式去除斜度。在 IAM 数据库中的离线手写文本行上进行了实验，与文献中报告的结果相比，在相同任务下的识别率是最好的之一。
