Search Results for author: Andre G. Hochuli

Found 5 papers, 1 papers with code

Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems

1 code implementation22 Jul 2022 Andre G. Hochuli, Alceu S. Britto Jr., Paulo R. L. de Almeida, Williams B. S. Alves, Fabio M. C. Cagni

When using vision-based approaches to classify individual parking spaces between occupied and empty, human experts often need to annotate the locations and label a training set containing images collected in the target parking lot to fine-tune the system.

End-to-End Approach for Recognition of Historical Digit Strings

no code implementations28 Apr 2021 Mengqiao Zhao, Andre G. Hochuli, Abbas Cheddad

The plethora of digitalised historical document datasets released in recent years has rekindled interest in advancing the field of handwriting pattern recognition.

Handwriting Recognition Segmentation

A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition

no code implementations29 Oct 2020 Andre G. Hochuli, Alceu S. Britto Jr, David A. Saji, Jose M. Saavedra, Robert Sabourin, Luiz S. Oliveira

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.

object-detection Object Detection +1

An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings

no code implementations28 Mar 2020 Andre G. Hochuli, Alceu S. Britto Jr., Jean P. Barddal, Luiz E. S. Oliveira, Robert Sabourin

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model.

Segmentation

Segmentation-Free Approaches for Handwritten Numeral String Recognition

no code implementations24 Apr 2018 Andre G. Hochuli, Luiz E. S. Oliveira, Alceu S. Britto Jr, Robert Sabourin

This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length.

Segmentation

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