Pipeline Enabling Zero-shot Classification for Bangla Handwritten Grapheme
This research investigates Zero-Shot Learning (ZSL), and proposes CycleGAN-based image synthesis and accurate label mapping to build a strong association between labels and graphemes. The objective is to enhance model accuracy in detecting unseen classes by employing advanced font image categorization and a CycleGAN-based generator. The resulting representations of abstract character structures demonstrate a significant improvement in recognition, accommodating both seen and unseen classes. This investigation addresses the complex issue of Optical Character Recognition (OCR) in the specific context of the Bangla language. Bangla script is renowned for its intricate nature, consisting of a total of 49 letters, which include 11 vowels, 38 consonants, and 18 diacritics. The combination of letters in this complex arrangement provides the opportunity to create almost 13,000 unique variations of graphemes, which exceeds the number of graphemic units found in the English language. Our investigation presents a new strategy for ZSL in the context of Bangla OCR. This approach combines generative models with careful labeling techniques to enhance the progress of Bangla OCR, specifically focusing on grapheme categorization. Our goal is to make a substantial impact on the digitalization of educational resources in the Indian subcontinent.
Code (0)
등록된 구현이 없습니다.
Tasks
Bangla Text DetectionClassificationGrapheme DetectionHandwritten Text RecognitionImage CategorizationImage GenerationOptical Character RecognitionOptical Character Recognition (OCR)Text Detectionzero-shot-classificationZero-Shot LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Zero-Shot Multi-Label Classification of Bangla Documents: Large Decoders Vs. Classic Encoders
Bangla, a language spoken by over 300 million native speakers and ranked as the sixth most spoken language worldwide, presents unique challenges in natural language processing (NLP) due to its complex morphological chara…
DecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBangla Key2Text: Text Generation from Keywords for a Low Resource Language
This paper introduces \textit{Bangla Key2Text}, a large-scale dataset of $2.6$ million Bangla keyword--text pairs designed for keyword-driven text generation in a low-resource language. The dataset is constructed using a…
Keyword ExtractionText GenerationBanglaSentNet: An Explainable Hybrid Deep Learning Framework for Multi-Aspect Sentiment Analysis with Cross-Domain Transfer Learning
Multi-aspect sentiment analysis of Bangla e-commerce reviews remains challenging due to limited annotated datasets, morphological complexity, code-mixing phenomena, and domain shift issues, affecting 300 million Bangla-s…
Domain GeneralizationSentiment AnalysisTransfer LearningFew-Shot LearningBanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering
Developing accurate biomedical Question Answering (QA) systems in low-resource languages remains a major challenge, limiting equitable access to reliable medical knowledge. This paper introduces BanglaMedQA and BanglaMMe…
Question AnsweringPerformance Evaluation of Large Language Models in Bangla Consumer Health Query Summarization
Consumer Health Queries (CHQs) in Bengali (Bangla), a low-resource language, often contain extraneous details, complicating efficient medical responses. This study investigates the zero-shot performance of nine advanced …