基于深度学习的电子物流票据信息分割与识别

针对中文电子物流票据中存在大量含有左右结构的中文字体,无法采用目前投影分割算法实现准确分割,提出了结合深度学习的电子物流票据分割和识别方法.首先,通过投影分割计算出每一个字符的宽度数据;然后针对所有的宽度数据进行聚簇为2的K-Means聚合,计算出正常与非正常字符的宽度范围;再使用卷积网络对非正常字符集进行识别,并对其中的偏旁进行准确组合,最后将完整的中文字符送进VGG16深度网络模型中进行识别.通过实验证明,我们提出的方法有效提高了整体字符分割的准确率,在识别方面,VGG16网络对于中文字体的识别准确率达99.9%.

An approach to offline handwritten Chinese character recognition based on segment evaluation of adaptive duration

This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments, which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string, and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration, and is very effective for the recognition of overlapped, broken, touched, loosely configured Chinese characters.