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Handwritten digit string recognition by combination of residual network and RNN-CTC
Recurrent neural network (RNN) and connectionist temporal classification...
10/09/2017 ∙ by Hongjian Zhan, et al. ∙
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On Study of the Reliable Fully Convolutional Networks with Tree Arranged Outputs (TAO-FCN) for Handwritten String Recognition
The handwritten string recognition is still a challengeable task, though...
07/10/2017 ∙ by Song Wang, et al. ∙
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Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections
In this paper, we approach the problem of segmentation-free query-by-str...
03/22/2017 ∙ by Tomas Wilkinson, et al. ∙
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Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network
We present a new handwritten text segmentation method by training a conv...
06/12/2019 ∙ by Junho Jo, et al. ∙
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Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks
This paper introduces a very challenging dataset of historic German docu...
04/01/2018 ∙ by Andreas Kölsch, et al. ∙
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Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention
We present an attention-based model for end-to-end handwriting recogniti...
04/12/2016 ∙ by Théodore Bluche, et al. ∙
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End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation
Traditional pedestrian collision warning systems sometimes raise alarms ...
12/20/2016 ∙ by Heechul Jung, et al. ∙
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Segmentation-Free Approaches for Handwritten Numeral String Recognition
04/24/2018 ∙ by A. G. Hochuli, et al. ∙
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This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length. A synthetic dataset of touching numeral strings of sizes 2-, 3- and 4-digits was created to train end-to-end solutions based on Convolutional Neural Networks. A robust experimental protocol is used to show that the proposed segmentation-free methods may reach the state-of-the-art performance without suffering the heavy burden of over-segmentation based methods. In addition, they confirmed the importance of introducing contextual information in the design of end-to-end solutions, such as the proposed length classifier when recognizing numeral strings.
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