A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition
Andre G. Hochuli, Alceu S. Britto Jr, David A. Saji, Jose M. Saavedra,, Robert Sabourin, Luiz S. Oliveira

TL;DR
This paper compares end-to-end methods for handwritten digit string recognition, highlighting the effectiveness of object detection models like Yolo over traditional segmentation-based approaches across multiple datasets.
Contribution
It provides a comprehensive evaluation and critical analysis of object detection and sequence-to-sequence methods for HDSR on five benchmark datasets.
Findings
Yolo outperforms segmentation-free models with shorter pipelines
Yolo achieves 97% recognition on NIST-SD19
Yolo achieves 84% recognition on CVL dataset
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…
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Taxonomy
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