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arXiv 2022年

作者： Ghosh, Subhankar Roy, Prasun Bhattacharya, Saumik Pal, Umapada Blumenstein, Michael Faculty of Engineering and IT University of Technology SydneyNSW Australia The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Indian Institute of Technology Kharagpur India

Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make the colorization pipeline automatic, these processes often produce unrealistic results due to a lack of conditioning. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To the best of our knowledge, this is one of the first attempts to incorporate textual conditioning in the colorization pipeline. To do so, we have proposed a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. We have evaluated our proposed model using different metrics and found that it outperforms the state-of-the-art colorization algorithms both qualitatively and quantitatively. © 2022, CC BY.

关键词： Pipelines

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MMC: Multi-Modal Colorization of Images using Textual Descriptions

arXiv

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arXiv 2023年

作者： Ghosh, Subhankar Bhattacharya, Saumik Roy, Prasun Pal, Umapada Blumenstein, Michael Faculty of Engineering and IT University of Technology SydneyNSW Australia The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Indian Institute of Technology Kharagpur India

Handling various objects with different colors is a significant challenge for image colorization techniques. Thus, for complex real-world scenes, the existing image colorization algorithms often fail to maintain color consistency. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To do so, we have proposed a deep network that takes two inputs (grayscale image and the respective encoded text description) and tries to predict the relevant color components. Also, we have predicted each object in the image and have colorized them with their individual description to incorporate their specific attributes in the colorization process. After that, a fusion model fuses all the image objects (segments) to generate the final colorized image. As the respective textual descriptions contain color information of the objects present in the image, text encoding helps to improve the overall quality of predicted colors. In terms of performance, the proposed method outperforms existing colorization techniques in terms of LPIPS, PSNR and SSIM metrics. © 2023, CC BY.

关键词： Color

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Multi-lingual City Name recognition for Indian Postal Automation

Multi-lingual City Name Recognition for Indian Postal Automa...

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International Workshop on Frontiers in Handwriting recognition

作者： Umapada Pal Ramit Kumar Roy Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Saint Xaviers College Kolkata India Graduate School of Engineering Mie University Tsu Japan

Under three-language formula, the destination address block of postal document of an Indian state is generally written in three languages: English, Hindi and the State official language. From the statistical analysis we found that 12.37%, 76.32% and 10.21% postal documents are written in Bangla, English and Devanagari script, respectively. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a city name is written. To avoid such script identification difficulties, in this paper we proposed a lexicon-driven method for multi-lingual (English, Hindi and Bangla) city name recognition for Indian postal automation. In the proposed scheme, at first, to take care of slanted handwriting of different individuals a slant correction technique is performed. Next, a water reservoir concept is applied to pre-segment the slant corrected city names into possible primitive components (characters or its parts). Pre-segmented components of a city name are then merged into possible characters to get the best city name using the lexicon information. In order to merge these primitive components into characters and to find optimum character segmentation, dynamic programming (DP) is applied using total likelihood of the characters of a city name as an objective function. We tested our system on 16132 Indian trilingual city names and 92.25% overall recognition accuracy was obtained.

关键词： Cities and towns Handwriting recognition Image segmentation Automation Dynamic programming Cavity resonators Feature extraction

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Bangla and English City Name recognition for Indian Postal Automation

Bangla and English City Name Recognition for Indian Postal A...

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International Conference on pattern recognition

作者： Umapada Pal Ramit Kumar Roy Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Saint Xavier's College Kolkata India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781424475421

Because of multi-lingual behavior destination address block of a postal document of an Indian state may be written in two or more scripts. From a statistical analysis of Indian postal document we noted that about 22.04% of Indian postal documents are written in two scripts. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a city name is written. To avoid such identification difficulties, in this paper we proposed a lexicon-driven bi-lingual (English and Bangla) city name recognition scheme for Indian postal automation. We obtained 93.19% accuracy when tested on 11875 city name samples.

关键词： Cities and towns Handwriting recognition Feature extraction Automation Image segmentation Cavity resonators Dynamic programming

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Word-Wise Handwritten Persian and Roman Script Identification

Word-Wise Handwritten Persian and Roman Script Identificatio...

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International Workshop on Frontiers in Handwriting recognition

作者： Kaushik Roy Alireza Alaei Umapada Pal Department of Computer Science West Bengal State University Kolkata India Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Most of the countries use bi-script documents. This is because every country uses its own national language and English as second/foreign language. Therefore, bi-lingual document with one language being the English and other being the national language is very common. Postal documents are a very good example of such bi-lingual/script document. This paper deals with word-wise handwritten script identification from bi-script documents written in Persian and Roman. In the proposed scheme, simple but fast computable set of 12 features based on fractal dimension, position of small component, topology etc. are used and a set of classifiers are employed for script identification experiments. We tested our scheme on a dataset of 5000 handwritten Persian and English words and 99.20% of correct script identification is obtained.

关键词： Fractals Support vector machines Kernel Polynomials Training Artificial neural networks Neurons

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CRNN based jersey-bib number/text recognition in sports and marathon images 15

CRNN based jersey-bib number/text recognition in sports and ...

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15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019

作者： Nag, Sauradip Ramachandra, Raghavendra Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Kankanhalli, Mohan Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781728128610

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets. © 2019 IEEE.

关键词： Sports

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ICDAR 2019 CROHME + TFD: Competition on recognition of Handwritten Mathematical Expressions and Typeset Formula Detection

ICDAR 2019 CROHME + TFD: Competition on Recognition of Handw...

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International Conference on Document Analysis and recognition

作者： Mahshad Mahdavi Richard Zanibbi Harold Mouchere Christian Viard-Gaudin Utpal Garain Document and Pattern Recognition Lab Rochester Institute of Technology Rochester NY USA University of Nantes Nantes France LS2N - UMR CNRS 6004 University of Nantes Nantes France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

We summarize the tasks, protocol, and outcome for the 6th Competition on recognition of Handwritten Mathematical Expressions (CROHME), which includes a new formula detection in document images task (+ TFD). For CROHME + TFD 2019, participants chose between two tasks for recognizing handwritten formulas from 1) online stroke data, or 2) images generated from the handwritten strokes. To compare LATEX strings and the labeled directed trees over strokes (label graphs) used in previous CROHMEs, we convert LATEX and stroke-based label graphs to label graphs defined over symbols (symbol-level label graphs, or symLG). More than thirty (33) participants registered for the competition, with nineteen (19) teams submitting results. The strongest formula recognition results were produced by the USTC-iFLYTEK research team, for both stroke-based (81%) and image-based (77%) input. For the new typeset formula detection task, the Samsung R&D Institute Ukraine (Team 2) obtained a very strong F-score (93%). System performance has improved since the last CROHME - still, the competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task.

关键词： Task analysis Handwriting recognition Tools Measurement Layout Image recognition

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Handwritten Street Name recognition for Indian Postal Automation

Handwritten Street Name Recognition for Indian Postal Automa...

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International Conference on Document Analysis and recognition

Although for postal automation there are many pieces of work towards street name recognition on non-Indian languages, to the best of our knowledge there is no work on street name recognition on Indian languages. In this paper we proposed a scheme for recognition of Indian street name written in Bangla script. Because of the writing style of different individuals some of the characters in a street name may touch with its neighboring characters. Accurate segmentation of such touching into individual characters is a difficult task. To avoid such segmentation, here we consider a street name string as word and the street name recognition problem is treated as lexicon driven word recognition. Some of the street names may contain two or more words and we have concatenated these words to have a single word. In the proposed method, at first, street names are binarized and pre-segmented into possible primitive components (individual characters or its parts) analyzing their cavity portions. Pre-segmented components of a street name are then merged into possible characters to get the best street name. Dynamic programming (DP) is applied for the merging using total likelihood of characters as the objective function. To compute the likelihood of a character, modified quadratic discriminant function (MQDF) is used. Our proposed system shows 99.03% reliability with 18.80% rejection, and 0.79% error rates when tested on 4450 handwritten Bangla street name samples.

关键词： Handwriting recognition Accuracy Cavity resonators Automation Feature extraction Dynamic programming Image segmentation

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On foreground-background separation in low quality color document images

On foreground-background separation in low quality color doc...

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International Conference on Document Analysis and recognition

作者： Utpal Garain T. Paquet L. Heutte Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Laboratoire PSI-FRE CNRS UFR des Sciences University of Rouen Mont Saint Aignan France

This paper proposes an adaptive method for separation of foreground and background in low quality color document images. A connected component labelling is initially implemented to capture the spatially connected similar color pixels. Next, dominant background components are determined to divide the entire image into number of grids each representing local uniformity in illumination, background, etc. Finally foreground parts are located using local information around them. Several color images of old historical documents including manuscripts of high importance are used in the experiment. Apart from a qualitative evaluation, results are quantitatively compared with one popular foreground/background separation technique.

关键词： Clustering algorithms Lighting Labeling Smoothing methods Image color analysis computer vision pattern recognition Image segmentation Internet Layout

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A New U-Net Based License Plate Enhancement Model in Night and Day Images 5th

A New U-Net Based License Plate Enhancement Model in Night a...

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5th Asian Conference on pattern recognition, ACPR 2019

作者： Chowdhury, Pinaki Nath Shivakumara, Palaiahnakote Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Gjøvik Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030414030

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection. © Springer Nature Switzerland AG 2020.

关键词： Pixels

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