An online cursive handwritten medical words recognition system for busy doctors in developing countries for ensuring efficient healthcare service delivery

Abstract

Doctors in developing countries are too busy to write digital prescriptions. Ninety-seven percent of Bangladeshi doctors write handwritten prescriptions, the majority of which lack legibility. Prescriptions are harder to read as they contain multiple languages. This paper proposes a machine learning approach to recognize doctors’ handwriting to create digital prescriptions. A ‘Handwritten Medical Term Corpus’ dataset is developed containing 17,431 samples of 480 medical terms. In order to improve the recognition efficiency, this paper introduces a data augmentation technique to widen the variety and increase the sample size. A sequence of line data is extracted from the augmented images of 1,591,100 samples and fed to a Bidirectional Long Short-Term Memory (LSTM) network. Data augmentation includes pattern Rotating, Shifting, and Stretching (RSS). Eight different combinations are applied to evaluate the strength of the proposed method. The result shows 93.0% average accuracy (max: 94.5%, min: 92.1%) using Bidirectional LSTM and RSS data augmentation. This accuracy is 19.6% higher than the recognition result with no data expansion. The proposed handwritten recognition technology can be installed in a smartpen for busy doctors which will recognize the writings and digitize them in real-time. It is expected that the smartpen will contribute to reduce medical errors, save medical costs and ensure healthy living in developing countries.

Adapting multilingual vision language transformers for low-resource Urdu optical character recognition (OCR)

“…In their study, Tabassum et al (2022) highlight the time constraints faced by doctors in developing countries, which hinder their ability to write digital prescriptions. They point out that the World Health Organization (WHO) recommends an ideal doctor-to-population ratio of 1:1,000, while in developing nations such as Pakistan, Bangladesh, and India, the ratio stands significantly lower at 0.304:1,000 ( Kumar & Pal, 2018 ).…”

“…However, digital prescriptions have gained global popularity for providing easier access to medical records. To address this issue, Tabassum et al (2022) propose an end-to-end recognition system that employs a Bidirectional Long Short-Term Memory (BiLSTM) approach to digitize multilingual handwritten prescriptions. They introduce a dataset named the “Handwritten Medical Term Corpus ,” consisting of 17,431 samples comprising 480 medical terms.…”

Construction of Alphabetic Character Recognition Systems: A Review

“…[49] Chinese → online-handwritten → page level → public dataset → SFC 4. [48] English → online-handwritten → line level → public dataset → FC 5. [13] Chinese → offline-printed → character level → self-constructed dataset → PFC…”

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

“…Notably, in the case of unstructured data, it is challenging to obtain a precise result as they are highly fluctuating. Example: translation of handwriting on clinical notes (Tabassum et al, 2022).…”