Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive Text

Yasin, Naseer M.; Siddiqi, Imran; Moetesum, Momina; Rauf, Sadaf Abdul

doi:10.1007/978-3-031-41501-2_6

Cited by 4 publications

(1 citation statement)

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“…N.Yasin et al [33] use transformer-based Neural Machine Translation (NMT) to post-process cursive Urdu OCR data with 57% error correction. It discusses Chinese word segmentation, Urdu sentence boundary disambiguation, and neural network and weighted finite state transducer OCR post-processing.…”

Section: Transformer Based Approachesmentioning

confidence: 99%

ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition

Hamza,

Ren,

Saeed

2024

PLoS ONE

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Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different writing styles, cursive scripts, and limited data make Urdu text recognition a complicated task. Major languages, such as English, have experienced advances in automated recognition, whereas low-resource languages, such as Urdu, still lag. Transformer-based models are promising for automated recognition in high- and low-resource languages such as Urdu. This paper presents a transformer-based method called ET-Network that integrates self-attention into EfficientNet for feature extraction and a transformer for language modeling. The use of self-attention layers in EfficientNet helps to extract global and local features that capture long-range dependencies. These features proceeded into a vanilla transformer to generate text, and a prefix beam search is used for the finest outcome. NUST-UHWR, UPTI2.0, and MMU-OCR-21 are three datasets used to train and test the ET Network for a handwritten Urdu script. The ET-Network improved the character error rate by 4% and the word error rate by 1.55%, while establishing a new state-of-the-art character error rate of 5.27% and a word error rate of 19.09% for Urdu handwritten text.

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Section: Transformer Based Approachesmentioning

confidence: 99%