Análise de um Fluxo Completo Automatizado de Etapas Voltado ao Reconhecimento de Texto em Imagens de Prescrições Médicas Manuscritas
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2024-01-10
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Universidade Federal de Goiás
Resumo
Compounding pharmacies deal with large volumes of medical prescriptions on a daily
basis, whose data needs to be manually inputted into information management systems
to properly process their customers’ orders. A considerable portion of these prescriptions
tend to be written by doctors with poorly legible handwriting, which can make decoding
them an arduous and time-consuming process. Previous works have investigated the use
of machine learning for medical prescription recognition. However, the accuracy rates
in these works are still fairly low and their approaches tend to be rather limited, as they
typically utilize small datasets, focus only on specific steps of the automated analysis
pipeline or use proprietary tools, which makes it difficult to replicate and analyse their
results. The present work contributes towards filling this gap by presenting an end-toend process for automated data extraction from handwritten medical prescriptions, from
text segmentation, to recognition and post-processing. The approach was built based on
an evaluation and adaptation of multiple existing methods for each step of the pipeline.
The methods were evaluated on a dataset of 993 images of medical prescriptions with
27,933 annotated words, produced with the support of a compounding pharmacy that
participated in the project. The results obtained by the best performing methods indicate
that the developed approach is reasonably effective, reaching an accuracy of 68% in the
segmentation step, and a character accuracy rate of 86.8% in the text recognition step.
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Citação
CORRÊA, A. P. Análise de um Fluxo Completo Automatizado de Etapas Voltado ao Reconhecimento de Texto em Imagens de Prescrições Médicas Manuscritas. 2024. 79 f. Dissertação (Mestrado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2024.