A comprehensive comparison of end-to-end approaches for handwritten digit string recognition

Andre G. Hochuli*, Alceu S. Britto, David A. Saji, José M. Saavedra, Robert Sabourin, Luiz S. Oliveira

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Over the last decades, most approaches proposed for handwritten digit string recognition (HDSR) have resorted to digit segmentation, which is dominated by heuristics, thereby imposing substantial constraints on the final performance. Few of them have been based on segmentation-free strategies where each pixel column has a potential cut location. Recently, segmentation-free strategies has added another perspective to the problem, leading to promising results. However, these strategies still show some limitations when dealing with a large number of touching digits. To bridge the resulting gap, in this paper, we hypothesize that a string of digits can be approached as a sequence of objects. We thus evaluate different end-to-end approaches to solve the HDSR problem, particularly in two verticals: those based on object-detection (e.g., Yolo and RetinaNet) and those based on sequence-to-sequence representation (CRNN). The main contribution of this work lies in its provision of a comprehensive comparison with a critical analysis of the above mentioned strategies on five benchmarks commonly used to assess HDSR, including the challenging Touching Pair dataset, NIST SD19, and two real-world datasets (CAR and CVL) proposed for the ICFHR 2014 competition on HDSR. Our results show that the Yolo model compares favorably against segmentation-free models with the advantage of having a shorter pipeline that minimizes the presence of heuristics-based models. It achieved a 97%, 96%, and 84% recognition rate on the NIST-SD19, CAR, and CVL datasets, respectively.

Original languageEnglish
Article number114196
JournalExpert Systems with Applications
Volume165
DOIs
StatePublished - 1 Mar 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Ltd

Keywords

  • Convolutional neural networks
  • Deep learning
  • Handwritten digit segmentation
  • Handwritten digit string recognition

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