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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >On Development and Performance Evaluation of Novel Odia Handwritten Digit Recognition Methods
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On Development and Performance Evaluation of Novel Odia Handwritten Digit Recognition Methods

机译：Odia手写数字识别新方法的开发与性能评估

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摘要

Odia is an old and recognized regional language of India and is spoken, read and written by almost 90% people of Odisha state and an appreciable part of the population of neighbouring states. Unfortunately, very little work on Odia language processing (OLP) has been carried out and reported in standard literature. Accordingly, in this paper, a sincere attempt has been made on the recognition of handwritten Odia numerals, as a part of OLP, by using a standard database (Bhowmik et al. in Proceedings of 9th international conference on information technology (ICIT’06), Bhubaneswar, 18–21 December, pp 105–110, 2006) of ISI Kolkata. In this investigation, we have chosen six different transforms like the discrete Fourier transform (DFT), short-time Fourier transform, discrete cosine transform, discrete wavelet transform, S-transform (ST) and curvelet transform (CT) for feature extraction from handwritten numerals and principal component analysis for feature reduction. The standard four adaptive classifiers such as multilayered perceptron (MLP), four types of functional link artificial neural network (FLANN), radial basis function network and probabilistic neural network (PNN) are used to classify the handwritten Odia digits using the reduced extracted features from transform as inputs. The investigation made in this paper reveals that the RBF network with CT-based features and the power series FLANN with DFT features provide the best and the worst recognition performance for handwritten Odia numerals, respectively. Further, the first five best accuracy-based combined recognition systems are RBF-CT, RBF-ST, RBF-WT, PNN-CT and MLP-CT which offers percentage classification accuracy of 98.70, 96.22, 95.12, 95.10 and 93.60, respectively.
机译：奥迪亚（Odia）是印度古老的，公认的地方语言，奥里萨邦（Odisha）州近90％的人口和邻邦的相当一部分人口都会说，读和写。不幸的是，有关Odia语言处理（OLP）的工作很少，并且在标准文献中也没有报道。因此，在本文中，通过使用标准数据库（Bhowmik等人在第9届国际信息技术会议论文集（ICIT'06）中，）进行了真诚的尝试，以识别手写Odia数字作为OLP的一部分。 ，布巴内斯瓦尔（Bhubaneswar），12月18-21日，第105-110页，2006年，ISI加尔各答。在这项研究中，我们选择了六种不同的变换，例如离散傅里叶变换（DFT），短时傅立叶变换，离散余弦变换，离散小波变换，S变换（ST）和Curvelet变换（CT），以便从手写体中提取特征数字和主成分分析以减少特征。使用标准的四个自适应分类器，例如多层感知器（MLP），四种类型的功能链接人工神经网络（FLANN），径向基函数网络和概率神经网络（PNN），使用从中减少的提取特征对手写Odia数字进行分类。转换为输入。本文进行的研究表明，具有基于CT的功能的RBF网络和具有DFT功能的功率系列FLANN分别为手写Odia数字提供了最佳和最差的识别性能。此外，前五个基于最佳准确性的组合识别系统是RBF-CT，RBF-ST，RBF-WT，PNN-CT和MLP-CT，它们分别提供98.70、96.22、95.12、95.10和93.60的百分比分类精度。

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