Normal Template Mapping: An Association-Inspired Handwritten Character Recognition Model

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jun Miao, Peng Liu, Chen Chen, Yuanhua Qiao
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引用次数: 0

Abstract

In identifying objects, people usually associate memory templates to guide visual attention and determine the category of an object. The initial character images that children learn are usually normal patterns. However, the variation in corresponding handwritten patterns is quite large. To learn these deformed images with large variance, current deep models must involve millions of parameters for such kind of classification tasks that seem much easier and simpler to children who learn to recognize new characters associated with their initially taught normal patterns. From the perspective of humans’ perception, when people see a new object, they first think of a template image in their memory, which is similar to the object. This mapping process makes it easier for humans to learn new objects. Inspired by this cognitive association mechanism, this study developed a cognition-inspired handwritten character recognition model using a proposed normal template mapping neural network. This model uses an encoder-decoder architecture to build a normal template mapping neural network that transforms handwritten character images of one class to normalized characters similar to a given printed template character image representing that class. Then, a simple shallow classifier recognizes these normalized images, which are easier to classify. The experimental results show that the proposed model completes handwritten character recognition with comparable or higher precision at a much lower parameter count than current representative deep models. The proposed model removes the individual styles of handwritten character images and maps them to patterns similar to normal template images. This greatly reduces the classification difficulty and enables the classifier to classify only known standard character images.

Abstract Image

正常模板映射:一种受关联启发的手写字符识别模型
在识别物体时,人们通常会联想记忆模板来引导视觉注意力,并确定物体的类别。儿童最初学习的字符图像通常是正常的图案。然而,相应手写图案的差异却相当大。要学习这些差异较大的变形图像,当前的深度模型必须涉及数百万个参数,才能完成此类分类任务,而对于儿童来说,学习识别与他们最初学习的正常图案相关联的新字符要容易得多,也简单得多。从人类感知的角度来看,当人们看到一个新物体时,首先会想到记忆中与该物体相似的模板图像。这种映射过程使人类更容易学习新物体。受这种认知联想机制的启发,本研究利用一个拟议的正常模板映射神经网络,开发了一个受认知启发的手写字符识别模型。该模型采用编码器-解码器架构来构建法线模板映射神经网络,将一类手写字符图像转换为与代表该类的给定印刷模板字符图像相似的法线化字符。然后,一个简单的浅层分类器就能识别这些更容易分类的归一化图像。实验结果表明,与目前具有代表性的深度模型相比,所提出的模型能以更低的参数数完成手写字符识别,精度相当或更高。所提出的模型剔除了手写字符图像的个人风格,并将其映射为类似于正常模板图像的模式。这大大降低了分类难度,使分类器只能对已知的标准字符图像进行分类。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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