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DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset

Big data research, 2021-02, Vol.23 [Peer Reviewed Journal]

ISSN: 2214-5796 ;ISSN: 2214-580X ;EISSN: 2214-580X ;DOI: 10.1016/j.bdr.2020.100182

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  • Title:
    DIGITNET: A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset
  • Author: Kusetogullari, Hüseyin ; Yavariabdi, Amir ; Hall, Johan ; Lavesson, Niklas
  • Subjects: DIDA handwritten digit dataset ; Digit string recognition ; Ensemble deep learning ; Handwritten digit detection ; Historical handwritten documents ; Skövde Artificial Intelligence Lab (SAIL)
  • Is Part Of: Big data research, 2021-02, Vol.23
  • Description: This paper introduces a novel deep learning architecture, named DIGITNET, and a large-scale digit dataset, named DIDA, to detect and recognize handwritten digits in historical document images written in the nineteen century. To generate the DIDA dataset, digit images are collected from 100,000 Swedish handwritten historical document images, which were written by different priests with different handwriting styles. This dataset contains three sub-datasets including single digit, large-scale bounding box annotated multi-digit, and digit string with 250,000, 25,000, and 200,000 samples in Red-Green-Blue (RGB) color spaces, respectively. Moreover, DIDA is used to train the DIGITNET network, which consists of two deep learning architectures, called DIGITNET-dect and DIGITNET-rec, respectively, to isolate digits and recognize digit strings in historical handwritten documents. In DIGITNET-dect architecture, to extract features from digits, three residual units where each residual unit has three convolution neural network structures are used and then a detection strategy based on You Look Only Once (YOLO) algorithm is employed to detect handwritten digits at two different scales. In DIGITNET-rec, the detected isolated digits are passed through 3 different designed Convolutional Neural Network (CNN) architectures and then the classification results of three different CNNs are combined using a voting scheme to recognize digit strings. The proposed model is also trained with various existing handwritten digit datasets and then validated over historical handwritten digit strings. The experimental results show that the proposed architecture trained with DIDA (publicly available from: https://didadataset.github.io/DIDA/) outperforms the state-of-the-art methods. © 2020 The Author(s)
  • Language: English
  • Identifier: ISSN: 2214-5796
    ISSN: 2214-580X
    EISSN: 2214-580X
    DOI: 10.1016/j.bdr.2020.100182
  • Source: SWEPUB Freely available online
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