Bangla online handwriting recognition using recurrent neural network architecture

Chakraborty, Bappaditya; Mukherjee, Partha; Bhattacharya, Ujjwal

doi:10.1145/3009977.3010072

Cited by 10 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dataset contains 13,353 images of handwritten lines of text created by 657 writers. A similar dataset in Bangla is the Bangla handwriting recognition dataset by Bappaditya et al [18] that has obtained 79,000 handwritten Bangla word samples written by 77 different writers. BanglaLekha-Isolated [19] and ISI [20] dataset comes with a vast number of handwriting samples of individual Bangla characters with numerals.…”

Section: Doctors' Handwriting Datasetmentioning

confidence: 99%

See 1 more Smart Citation

An Online Cursive Handwritten Medical Words Recognition System for Busy Doctors in Developing Countries for Ensuring Efficient Healthcare Service Delivery

Tabassum¹,

Rahman²,

Abedin³

et al. 2021

Preprint

View full text Add to dashboard Cite

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 LSTM. 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.

show abstract

Section: Doctors' Handwriting Datasetmentioning

confidence: 99%

“…The RNNs work with sequence data of coordinates which contain vast information than static images [14]. Bappaditya et al have used bidirectional LSTM using 65,620 handwritten Bangla words dataset and has obtained 79% accuracy [18].…”

Section: Online Handwritten Character Recognition: Using Time-series Data As Inputmentioning

confidence: 99%