Journal ArticleDOI
Handwritten digit segmentation: a comparative study
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TLDR
It is concluded that combining different segmentation algorithms may be an appropriate strategy for improving the correct segmentation rate.Abstract:
In this work, algorithms for segmenting handwritten digits based on different concepts are compared by evaluating them under the same conditions of implementation. A robust experimental protocol based on a large synthetic database is used to assess each algorithm in terms of correct segmentation and computational time. Results on a real database are also presented. In addition to the overall performance of each algorithm, we show the performance for different types of connections, which provides an interesting categorization of each algorithm. Another contribution of this work concerns the complementarity of the algorithms. We have observed that each method is able to segment samples that cannot be segmented by any other method, and do so independently of their individual performance. Based on this observation, we conclude that combining different segmentation algorithms may be an appropriate strategy for improving the correct segmentation rate.read more
Citations
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Journal ArticleDOI
ARDIS: a Swedish historical handwritten digit dataset
TL;DR: Experimental results show that machine learning algorithms, including deep learning methods, provide low recognition accuracy as they face difficulties when trained on existing datasets and tested on ARDIS dataset, which proves that AR DIS dataset has unique characteristics.
Journal ArticleDOI
Handwritten digit segmentation: Is it still necessary?
TL;DR: This work postulates that handwritten digit segmentation can be successfully replaced by a set of classifiers trained to predict the size of the string and classify them without any segmentation, and shows that the CNN classifiers can handle complex cases of touching digits more efficiently than all segmentation algorithms available in the literature.
Journal ArticleDOI
Unknown-Length Handwritten Numeral String Recognition Using Cascade of PCA-SVMNet Classifiers
Saleh Aly,Ahmed A. Mohamed +1 more
TL;DR: The experimental results show that the cascade of PCA-SVMNet classifier efficiently recognizes unknown handwritten digit string without applying any sophisticated segmentation methods, achieving state-of-the-art recognition accuracy compared to other segmentation-free techniques.
Journal ArticleDOI
DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset
TL;DR: The experimental results show that the proposed DIGITNET architecture trained with DIDA outperforms the state-of-the-art methods.
Journal ArticleDOI
Segmentation of connected handwritten digits using Self-Organizing Maps
TL;DR: An algorithm for segmentation of connected handwritten digits based on the selection of feature points, through a skeletonization process, and the clustering of the touching region via Self-Organizing Maps is presented.
References
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Journal ArticleDOI
A survey of methods and strategies in character segmentation
R.G. Casey,Eric Lecolinet +1 more
TL;DR: H holistic approaches that avoid segmentation by recognizing entire character strings as units are described, including methods that partition the input image into subimages, which are then classified.
Journal ArticleDOI
Segmentation methods for character recognition: from segmentation to document structure analysis
TL;DR: A pattern- oriented segmentation method for optical character recognition that leads to document structure analysis is presented, and an extended form of pattern-oriented segmentation, tabular form recognition, is considered.
Journal ArticleDOI
Automatic recognition of handwritten numerical strings: a recognition and verification strategy
TL;DR: A modular system to recognize handwritten numerical strings using a segmentation-based recognition approach and a recognition and verification strategy that combines the outputs from different levels such as segmentation, recognition, and postprocessing in a probabilistic model is proposed.
Journal ArticleDOI
Touching numeral segmentation using water reservoir concept
TL;DR: A robust scheme to take care of variability involved in the writing style of different individuals a robust scheme is presented here, mainly based on features obtained from a concept based on water reservoir.
Journal ArticleDOI
Segmentation of single- or multiple-touching handwritten numeral string using background and foreground analysis
Yi-Kai Chen,Jhing-Fa Wang +1 more
TL;DR: An approach of segmenting a single- or multiple-touching handwritten numeral string (two-digits) is proposed that can get a correct rate of 96 percent with rejection rate of 7.8 percent, which compares favorably with those reported in the literature.
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