  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.
