Search Results for author:
Found 25 papers, 0 papers with code
CompTLL-UNet: Compressed Domain Text-Line Localization in Challenging Handwritten Documents using Deep Feature Learning from JPEG Coefficients
Automatic localization of text-lines in handwritten documents is still an open and challenging research problem.
A Survey on Change Detection Techniques in Document Images
However, this paper presents a survey on core techniques and rules to detect changes in different versions of a document image.
A Survey on Figure Classification Techniques in Scientific Documents
Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts.
A Survey and Approach to Chart Classification
Charts represent an essential source of visual information in documents and facilitate a deep understanding and interpretation of information typically conveyed numerically.
DWT-CompCNN: Deep Image Classification Network for High Throughput JPEG 2000 Compressed Documents
For any digital application with document images such as retrieval, the classification of document images becomes an essential stage.
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network
The first model is directly trained with JPEG compressed DCT images (compressed domain) to generate the compressed images from text descriptions.
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks
Image binarization techniques are being popularly used in enhancement of noisy and/or degraded images catering different Document Image Anlaysis (DIA) applications like word spotting, document retrieval, and OCR.
OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model
The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission.
HWRCNet: Handwritten Word Recognition in JPEG Compressed Domain using CNN-BiLSTM Network
Handwritten word recognition from document images using deep learning is an active research area in the field of Document Image Analysis and Recognition.
Angle Based Feature Learning in GNN for 3D Object Detection using Point Cloud
Feature encoding is one of the important steps in Detection of 3D objects.
Detection of Plant Leaf Disease Directly in the JPEG Compressed Domain using Transfer Learning Technique
Therefore accurate and timely detection of leaf disease is very important to check the loss of the crops and meet the growing food demand of the people.
Deep Learning Based Image Retrieval in the JPEG Compressed Domain
Here, we propose a unified model for image retrieval which takes DCT coefficients as input and efficiently extracts global and local features directly in the JPEG compressed domain for accurate image retrieval.
A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain
This research field is termed as Compressed Domain Processing.
Automatic Page Segmentation Without Decompressing the Run-Length Compressed Text Documents
However, carrying out page segmentation directly in compressed documents without going through the stage of decompression is a challenging goal.
Word and character segmentation directly in run-length compressed handwritten document images
In this paper, we investigate the issues of word and character segmentation directly on the run-length compressed document images.
Automatic Text Line Segmentation Directly in JPEG Compressed Document Images
The first approach is based on the strategy of partial decompression of selected DCT blocks, and the second approach is with intelligent analysis of F10 and F11 AC coefficients and without using any type of decompression.
DCT-CompCNN: A Novel Image Classification Network Using JPEG Compressed DCT Coefficients
However, if we intend to classify images directly with its compressed data, the same approach may not work better, like in case of JPEG compressed images.
NLP Driven Ensemble Based Automatic Subtitle Generation and Semantic Video Summarization Technique
This paper proposes an automatic subtitle generation and semantic video summarization technique.
Direct Processing of Document Images in Compressed Domain
The different operations demonstrated are feature extraction; text-line, word and character segmentation; document block segmentation; and font size detection, all carried out in the compressed version of the document.
Automatic Removal of Marginal Annotations in Printed Text Document
Recovering the original printed texts from a document with added handwritten annotations in the marginal area is one of the challenging problems, especially when the original document is not available.
Entropy Computation of Document Images in Run-Length Compressed Domain
Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer.
Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents
In this research, we propose to extract essential features such as projection profile, run-histogram and entropy for text document analysis directly from run-length compressed text-documents.
Extraction of Line Word Character Segments Directly from Run Length Compressed Printed Text Documents
Segmentation of a text-document into lines, words and characters, which is considered to be the crucial pre-processing stage in Optical Character Recognition (OCR) is traditionally carried out on uncompressed documents, although most of the documents in real life are available in compressed form, for the reasons such as transmission and storage efficiency.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Automatic Detection of Font Size Straight from Run Length Compressed Text Documents
In the proposed model, the given mixed-case text documents of different font size are segmented into compressed text lines and the features extracted such as line height and ascender height are used to capture the pattern of font size in the form of a regression line, using which the automatic detection of font size is done during the recognition stage.
Direct Processing of Run Length Compressed Document Image for Segmentation and Characterization of a Specified Block
Extracting a block of interest referred to as segmenting a specified block in an image and studying its characteristics is of general research interest, and could be a challenging if such a segmentation task has to be carried out directly in a compressed image.
