[论文解读] Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier

Nibaran Das, Bindaban Das+4 more|arXiv (Cornell University)|Feb 22, 2010

Handwritten Text Recognition Techniques参考文献 12被引用 80

一句话总结

本文提出一种混合机器学习方法，利用多层感知机（MLP）和支持向量机（SVM）分类器识别手写孟加拉语基础字符和复合字符。通过按频率优先逐步识别常见复合字符，该方法在三折交叉验证下实现了79.25%的平均识别率，有效应对了孟加拉文字的高复杂度和视觉相似性问题。

ABSTRACT

A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large varieties of handwritten characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision are inherent in handwritten script. Moreover, such a large varieties of complex shaped characters, some of which have close resemblance, makes the problem of OCR of handwritten Bangla characters more difficult. Considering the complexity of the problem, the present approach makes an attempt to identify compound character classes from most frequently to less frequently occurred ones, i.e., in order of importance. This is to develop a frame work for incrementally increasing the number of learned classes of compound characters from more frequently occurred ones to less frequently occurred ones along with Basic characters. On experimentation, the technique is observed produce an average recognition rate of 79.25 after three fold cross validation of data with future scope of improvement and extension.

研究动机与目标

• 为解决识别大量复杂且视觉相似的手写孟加拉语字符的挑战，包括50种基础字符和近160种复合字符类。
• 通过在分步学习框架中优先处理最常出现的复合字符，降低识别复杂度。
• 开发一种增量学习框架，从基础字符逐步扩展到高频复合字符。
• 提升手写孟加拉语文字识别的OCR性能，该任务因高度可变性和形态多样性而复杂化。

提出的方法

• 对手写孟加拉语字符图像应用特征提取流程，以捕捉区分性模式。
• 将数据集按频率对复合字符进行划分，以支持增量训练。
• 在提取的特征上训练并评估多层感知机（MLP）和支持向量机（SVM）分类器。
• 使用三折交叉验证评估所有字符类别上的泛化性能。
• 系统按频率顺序逐步添加复合字符类别，从最常见者开始。
• 汇总识别结果，计算所有类别上的总体平均识别率。

实验结果

研究问题

• RQ1 混合MLP与SVM方法能否有效识别手写孟加拉语中的基础字符与复合字符？
• RQ2 基于字符频率的增量学习如何影响识别准确率？
• RQ3 对于孟加拉语这样庞大而复杂的字符集，可实现的识别率是多少？
• RQ4 特征表示与分类器选择如何影响对视觉相似复合字符的性能表现？
• RQ5 基于优先级的学习策略能否降低识别高变异度手写文字的复杂度？

主要发现

• 所提方法在三折交叉验证下，对所有基础与复合孟加拉语字符实现了79.25%的平均识别率。
• 优先识别高频复合字符显著提升了学习效率与模型稳定性。
• MLP与SVM分类器的结合在处理手写孟加拉语文字的高变异性方面表现出强健性。
• 增量学习框架能有效扩展以应对大量复合字符类别。
• 结果表明，通过改进特征工程与扩大训练数据，性能仍有显著提升空间。
• 本研究证实，基于频率的类别优先策略是管理手写字符识别系统复杂性的可行方案。