mlsanityData to 14 Oct 2026

Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline

arXiv 2505.11177 ↗First posted 16 May 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

This paper presents an end-to-end suite for multilingual information extraction and processing from image-based documents. The system uses Optical Character Recognition (Tesseract) to extract text in languages such as English, Hindi, and Tamil, and then a pipeline involving large language model APIs (Gemini) for cross-lingual translation, abstractive summarization, and re-translation into a target language. Additional modules add sentiment analysis (TensorFlow), topic classification (Transformers), and date extraction (Regex) for better document comprehension. Made available in an accessible Gradio interface, the current research shows a real-world application of libraries, models, and APIs to close the language gap and enhance access to information in image media across different linguistic environments

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 16 May 2025.

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18 of 33 references.

References · 18

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  11. 2019Text Summarization with Pretrained Encoderscited by 556
  12. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 553
6 more
  1. 2021TrOCR: Transformer-based Optical Character Recognition with Pre-trained Modelscited by 279
  2. 2019What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysiscited by 191
  3. 2019HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarizationcited by 121
  4. 2020AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languagescited by 44
  5. 2020iNLTK: Natural Language Toolkit for Indic Languagescited by 24
  6. 2016OCR++: A Robust Framework For Information Extraction from Scholarly Articlescited by 5

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