Document indexing framework for retrieval of degraded document images

Garg, Ritu; Hassan, Ehtesham; Chaudhury, Santanu

doi:10.1109/icdar.2015.7333966

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Automatic summarization (Chu, Song & Jaimes, 2015) or indexing (Garg, Hassan & Chaudhury, 2015) has been investigated in a variety of study disciplines, including video, audio, and document (text) processing. Text summarizing techniques were employed to construct concise summaries of documents.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Model for Text Summarization Using Natural Language Processing

Karanja¹,

Matheka²

2022

OJIT

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Text summarization plays an important role in the area of natural language processing. The need for information all over the world to solve specific problems keeps on increasing daily. This poses a greater challenge as data stored on the internet has gradually increased exponentially over time. Finding out the relevant data and manually summarizing it in a short time is a challenging and tedious task for a human being. Text Summarization aims to compress the source text into a more concise form while preserving its overall meaning. Two major categories of text summarization methods exist namely: extractive and abstractive. The extractive technique concentrates on determining key themes using frequency analysis of sentences in the corpus of the text. Abstractive methods write a new summary with newly generated texts which do not appear in the corpus itself. This paper presents a hybrid model for text summarization using both extractive and abstractive techniques. Term Frequency (TF) – Inverse Document Frequency (IDF) was used for extractive and T5 Transformers for abstractive summarization. Iterative Incremental Methodology was adopted in the study. The hybrid model emerged as not the best choice compared to the extractive and abstractive as it had been hypothesized in the study when the accuracy and execution time of the summary generated was considered.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Model for Text Summarization Using Natural Language Processing

Karanja¹,

Matheka²

2022

OJIT

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“…The authors proved the efficiency of their method in table and tax form. In [10], the authors presented a document indexing method for retrieval of degeneration Bangla and Devanagari manuscript images and combined word image shape descriptor‐based features with the underlying subject distribution of the corresponding word.…”

Section: Related Workmentioning

confidence: 99%

SIFT and HOG features for the retrieval of ancient Kannada epigraphs

2020

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In this work, the authors presented the indexing method for efficient retrieval of Kannada characters. Here the characters were segmented by the connected component method. Then features were extracted by scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) methods. These features were condensed by principal component analysis. They presented the indexing approach using K‐dimensional tree (K‐D tree) to improve the identification process. For the experiment, they used their own database. The results of the experiment show that the indexing prior is faster than conventional identification approaches in terms of time to script. From experimentation, they observe that fusion features achieve the maximum accuracy of 90% with varying principal component analysis features.

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Info-Graphics Retrieval: A Multi-kernel Distance Based Hashing Scheme

Garg

Chaudhury

2017

Computer Vision, Graphics, and Image Processing

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Document indexing framework for retrieval of degraded document images

Cited by 4 publications

References 21 publications

A Hybrid Model for Text Summarization Using Natural Language Processing

A Hybrid Model for Text Summarization Using Natural Language Processing

SIFT and HOG features for the retrieval of ancient Kannada epigraphs

Info-Graphics Retrieval: A Multi-kernel Distance Based Hashing Scheme

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