Assisting fuzzy offline handwriting recognition using recurrent belief propagation

Yilan Li, Zhe Li, Qinru Qiu
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引用次数: 4

Abstract

Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. This problem has been tackled by many previous researchers using techniques such as deep learning networks and hidden Markov Models (HMM), etc. In this work we aim at offline fuzzy recognition of handwritten texts. A probabilistic inference network that performs recurrent belief propagation is developed to post process the recognition results of deep convolutional neural network (CNN) (e.g. LeNet) and form individual characters to words. The post processing has the capability of correcting deletion, insertion and replacement errors in a noisy input. The output of the inference network is a set of words with their probability of being the correct one. To limit the size of candidate words, a series of improvements have been made to the probabilistic inference network, including using a post Gaussian Mixture Estimation model to prune insignificant words. The experiments show that this model gives a competitively average accuracy of 85.5%, and the improvements provides 46.57% reduction of invalid candidate words.
利用循环信念传播辅助模糊离线手写识别
手写文本的识别是一项具有挑战性的任务,因为书写风格不同,相邻字符之间缺乏清晰的边界。许多研究者使用深度学习网络和隐马尔可夫模型(HMM)等技术解决了这个问题。在这项工作中,我们的目标是手写文本的离线模糊识别。开发了一种循环信念传播的概率推理网络,对深度卷积神经网络(CNN)(如LeNet)的识别结果进行后处理,形成单个字符到单词。后置处理具有校正噪声输入中的删除、插入和替换错误的能力。推理网络的输出是一组具有正确概率的单词。为了限制候选词的大小,对概率推理网络进行了一系列改进,包括使用后高斯混合估计模型来修剪不重要的词。实验表明,该模型的竞争平均准确率为85.5%,改进后的无效候选词减少了46.57%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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