首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >BANKCHECK RECOGNITION USING CROSS VALIDATION BETWEEN LEGAL AND COURTESY AMOUNTS
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BANKCHECK RECOGNITION USING CROSS VALIDATION BETWEEN LEGAL AND COURTESY AMOUNTS

机译:在法律和礼遇性金额之间使用交叉验证的银行支票识别

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A bankcheck reading system using cross validation of both the legal and the courtesy amounts is presented in this paper. Some of the challenges posed by the task are (ⅰ) segmentation of the legal amount into words, (ⅱ) location of boundaries between dollars and cents amounts, and (ⅲ) high accuracy in terms of recognition performance. Word segmentation in the legal amount is a serious issue because of the nature of the data and patrons' writing habits which tend to clump words together. We have developed a word segmentation algorithm based on the character segmentation results to address this issue. The list of possible amounts generated by the word segmentation hypotheses is used as lexicon for the courtesy amount recognition. The order of magnitude of the amount is estimated during legal amount recognition. We treat the courtesy amount as a numeral string and apply the same word recognition scheme as used for the legal amount. Our approach to check recognition differs from traditional methods in two significant aspects: First, our emphasis on both the legal and the courtesy amounts is balanced. We use an accurate word recognizer which performs equally well on alpha words and digit strings. Second, our combination strategy is serial rather than the commonly used parallel method. Experimental results show that 43.8% of check images are correctly read with an error rate of 0%.
机译:本文提出了一种使用法律和礼节性金额交叉验证的银行支票阅读系统。任务带来的一些挑战是(ⅰ)将法定金额分割成单词,(ⅱ)美元和美分金额之间的边界位置,以及(ⅲ)识别性能方面的准确性很高。由于数据的性质和顾客的写作习惯往往会使单词聚集在一起,因此合法数量的单词分割是一个严重的问题。我们已经基于字符分割结果开发了一种分词算法来解决此问题。由分词假设生成的可能金额列表用作礼貌金额识别的词典。在合法金额确认期间估算金额的数量级。我们将礼遇金额视为数字字符串,并采用与合法金额相同的单词识别方案。我们的支票确认方法在两个重要方面与传统方法不同:首先,我们在法律和礼节性金额上的重视是平衡的。我们使用一个精确的单词识别器,该识别器在字母单词和数字字符串上的表现同样出色。其次,我们的组合策略是串行的,而不是常用的并行方法。实验结果表明,正确读取了43.8%的支票图像,错误率为0%。

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