A survey on camera-captured scene text detection and extraction: towards Gurmukhi script

Kaur, Amandeep; Dhir, Renu; Lehal, Gurpreet Singh

doi:10.1007/s13735-016-0116-5

Cited by 11 publications

(1 citation statement)

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“…While numerous studies have focused on identifying handwritten digits in single languages like English or Chinese [22,23], relatively few research endeavors have explored the realm of multi-lingual digit recognition [1,5,13]. This task poses unique challenges due to variations in writing styles, stroke thickness, and the presence of language-speci c features like dots, making it di cult to develop a uni ed model for accurate identi cation across all languages [1].…”

Section: Introductionmentioning

confidence: 99%

Unveiling Cross-Linguistic Mastery: Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning

Fateh,

Birgani,

Fateh

2023

Preprint

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In the realm of data analysis and document processing, the recognition of handwritten numerals stands as a pivotal advancement. This contribution has steered transformative shifts in optical character recognition, historical handwritten document analysis, and postal automation. A persistent challenge in this arena is the recognition of handwritten digits across a spectrum of languages, each with its idiosyncrasies. We present an innovative paradigm to surmount this hurdle, transcending the confines of monolingual recognition. Unlike the status quo, which gravitates toward a narrow subset of languages, our method orchestrates a comprehensive solution spanning 12 distinct languages, deftly navigating linguistic intricacies. The catalyst for this efficacy is transfer learning, amplifying image quality and recognition acumen. Emboldening this framework is an ingenuity-charged attention-based module that refines precision. Our rigorous experimentations substantiate quantum leaps in image quality and the prowess of linguistic and numeral recognition. Notably, we unearth significant accuracy strides, eclipsing 2% enhancements in specific languages vis-à-vis antecedent methodologies. This endeavor epitomizes a sturdy, economically sound avenue, unshackling multilingual handwritten numeral recognition to an expansive spectrum of languages.

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