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Struck-out handwritten word detection and restoration for automatic descriptive answer evaluation

SIGNAL PROCESSING-IMAGE COMMUNICATION 2025年 130卷

作者： Zhong, Dajian Palaiahnakote, Shivakumara Pal, Umapada Lu, Yue Shanghai Maritime Univ Coll Informat Engn Shanghai Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Univ Salford Sch Sci Engn & Environm Manchester England Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata India

Unlike objective type evaluation, descriptive answer evaluation is challenging due to unpredictable answers and free writing style of answers. Because of these, descriptive answer evaluation has received special attention from many researchers. Automatic answer evaluation is useful for the following situations. It can avoid human intervention for marking, eliminates bias marking and most important is that it can save huge manpower. To develop an efficient and accurate system, there are several open challenges. One such open challenge is cleaning the document, which includes struck-out words removal and restoring the struck-out words. In this paper, we have proposed a system for struck-out handwritten word detection and restoration for automatic descriptive answer evaluation. The work has two stages. In the first stage, we explore the combination of ResNet50 and the diagonal line (principal and secondary diagonal lines) segmentation module for detecting words and then classifying struck-out words using a classification network. In the second stage, we explore the combination of UNet as a backbone and Bi-LSTM for predicting pixels that represent actual text information of the struck-out words based on the relationship between sequences of pixels for restoration. Experimental results on our dataset and standard datasets show that the proposed model is impressive for struck-out word detection and restoration. A comparative study with the state-of-the-art methods shows that the proposed approach outperforms the existing models in terms of struck-out word detection and restoration.

关键词： Cleaning the handwritten document Erasing handwritten words Stuck-out words Non-struck-out words Answer script evaluation

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Diverse Receptive Field Based Adversarial Concurrent Encoder Network for Image Inpainting

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1873-1877页

作者： Phutke, Shruti S. Murala, Subrahmanyam IIT Ropar Comp Vis & Pattern Recognit Lab Rupnagar 140001 India

Image inpainting is nowadays demanding because of its wide applications such as removing the unwanted objects from the image or recovering the old corrupted photo. Existing approaches achieved superior performance with coarse-to-fine or progressive or recurrent architectures for image inpainting regardless of computational complexity. In these types, the disturbance at the first instance or first iteration may lead to semantically unambiguous results. Also, to inpaint the image with varying hole sizes it is desirable to focus on the diverse receptive fields without deeper network i.e, network with less number of parameters. Therefore, we have proposed a lightweight adversarial concurrent encoder architecture with a diverse receptive field for image inpainting. Here, the concurrent encoder is integrated with diverse receptive fields to benefit with lower computational complexity. The proposed method is compared with state-of-the-art (SOTA) methods on Places2 and Paris Street View dataset in terms of peak signal-to-noise ratio and structural similarity index. Along with the extensive results' analysis and ablation study, the proposed method proves the effectiveness in terms of less computational complexity compared to existing SOTA methods.

关键词： Training computer architecture Convolution computational complexity Feature extraction Task analysis Indexes Concurrent encoder network diverse receptive block adversarial learning image inpainting

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Interactive Multi-Dimension Modulation for Image Restoration

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2022年第12期44卷 9363-9379页

作者： He, Jingwen Dong, Chao Liu, Yihao Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Beijing 100864 Peoples R China Shanghai AI Lab Shanghai 201803 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. compared with the previous single-dimension (SD) modulation, the MD setup to handle multiple degradations adaptively and relief data unbalancing problem in different degradation types. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution together with a simple loss reweighting approach to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Moreover, we also provide an estimation network to predict the condition vector, thus the base network could directly output the restored image without modulation from users. Extensive experiments demonstrate that the proposed CResMD achieves excellent performance on both SD and MD modulation tasks.

关键词： Degradation Modulation Image restoration Task analysis Noise reduction Estimation Adaptation models Image restoration deep learning

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Neural Transformation Fields for Arbitrary-Styled Font Generation

Neural Transformation Fields for Arbitrary-Styled Font Gener...

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IEEE/CVF Conference on computer vision and pattern recognition (CVPR)

作者： Fu, Bin He, Junjun Wang, Jianjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-content disentanglement paradigm, which transfers the target font styles to characters by combining the content representations of source characters and the style codes of reference samples. Most existing methods attempt to increase font generation ability via exploring powerful style representations, which may be a sub-optimal solution for the FFG task due to the lack of modeling spatial transformation in transferring font styles. In this paper, we model font generation as a continuous transformation process from the source character image to the target font image via the creation and dissipation of font pixels, and embed the corresponding transformations into a neural transformation field. With the estimated transformation path, the neural transformation field generates a set of intermediate transformation results via the sampling process, and a font rendering formula is developed to accumulate them into the target font image. Extensive experiments show that our method achieves state-of-the-art performance on few-shot font generation task, which demonstrates the effectiveness of our proposed model. Our implementation is available at: https://***/fubinfb/NTF.

关键词： Image and video synthesis and generation

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A Contrastive Learning Network for Performance Metric and Assessment of Physical Rehabilitation Exercises

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 3790-3802页

作者： Yao, Long Lei, Qing Zhang, Hongbo Du, Jixiang Gao, Shangce Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361000 Peoples R China Huaqiao Univ Dept Comp Sci & Technol Xiamen 361000 Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361000 Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan

Human activity analysis in the legal monitoring environment plays an important role in the physical rehabilitation field, as it helps patients with physical injuries improve their postoperative conditions and reduce their medical costs. Recently, several deep learning-based action quality assessment (AQA) frameworks have been proposed to evaluate physical rehabilitation exercises. However, most of them treat this problem as a simple regression task, which requires both the action instance and its score label as input. This approach is limited by the fact that the annotations in this field usually consist of healthy or unhealthy labels rather than quality scores provided by professional physicians. Additionally, most of these methods cannot provide informative feedback on a patient's motion defects, which weakens their practical application. To address these problems, we propose a multi-task contrastive learning framework to learn subtle and critical differences from skeleton sequences to deal with the performance metric and AQA problems of physical rehabilitation exercises. Specifically, we propose a performance metric network that takes triplets of training samples as input for score generation. For the AQA task, the same contrast learning strategy is used, but pairwise training samples are fed into the action quality assessment network for score prediction. Notably, we propose quantifying the deviation of the joint attention matrix between different skeleton sequences and introducing it into the loss function of our learning network. It is proven that considering both score prediction loss and joint attention deviation loss improves physical exercises AQA performance. Furthermore, it helps to obtain informative feedback for patients to improve their motion defects by visualizing the joint attention matrix's difference. The proposed method is verified on the UI-PRMD and KIMORE datasets. Experimental results show that the proposed method achieves state-of-the-art pe

关键词： Physical rehabilitation exercise action quality assessment performance metric quantification contrastive learning informative feedback

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MSAR-Net: Multi-scale attention based light-weight image super-resolution

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pattern recognitION LETTERS 2021年 151卷 215-221页

作者： Mehta, Nancy Murala, Subrahmanyam Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar 140001 India

Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics industries. The exceptional success of convolution neural networks (CNNs), has absolutely revolutionized the field of SISR. However, for most of the CNN-based SISR methods, excessive memory consumption in terms of parameters and flops, hinders their application in low-computing power devices. Moreover, different state-of-the-art SR methods collect different f eatures, by treating all the pixels contributing equally to the performance of the network. In this paper, we take into consideration both the performance and the reconstruction efficiency, and propose a Light-weight multi-scale attention residual network (MSARNet) for SISR. The proposed MSAR-Net consists of stack of multi-scale attention residual (MSAR) blocks for feature refinement, and an up and down-sampling projection (UDP) block for edge refinement of the extracted multi-scale features. These blocks are capable of effectively exploiting the multi-scale edge information, without increasing the number of parameters. Specially, we design our network in progressive fashion, for substituting the large scale factors (x 4) combinations, with small scale factor ( x2) combinations, and thus gradually exploit the hierarchical information. In parallel, for modulation of multi-scale features in global and local manners, channel and spatial attention in MSAR block is being used. visual results and quantitative metrics of PSNR and SSIM exhibit the accuracy of the proposed approach on synthetic benchmark super-resolution datasets. The experimental analysis shows that the proposed approach outperforms the other existing methods for SISR in terms of memory footprint, inference time and visual quality. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-scale attention residual block Up and down-sampling projection block Image super-resolution

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Unified Multi-Weather visibility Restoration

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 7686-7698页

作者： Kulkarni, Ashutosh Patil, Prashant W. Murala, Subrahmanyam Gupta, Sunil Indian Inst Technol Ropar Dept Elect Engn Comp Vis & Pattern Recognit Lab Rupnagar 140001 Punjab India Deakin Univ Appl Artificial Intelligence Inst A2I2 Geelong Vic 3125 Australia

Automated surveillance is widely opted for applications such as traffic monitoring, vehicle identification, etc. But, various weather degradation factors such as rain and snow streaks, along with atmospheric veil severely affect the perceptual quality of an image, eventually affecting the performance of these applications. There exist weather specific (rain, haze, snow, etc.) methods focusing on respective restoration task. As image restoration is a preprocessing step for high level surveillance applications, it is practically inapplicable to have different architectures for different weather restoration. In this paper, we propose a lightweight unified network, having 1.1 M parameters (1/40th and 1/6th of the existing rain with veil removal, and snow with veil removal methods respectively) for removal of rain and snow along with the veiling effect present in the images. In this network, we propose two parallel streams to handle the degradations and restoration: First, degradation removal stream (DRS) focuses mainly on removing randomly repeating degradations i.e., rain and snow streaks, through the proposed adaptive multi-scale feature sharing block (AMFSB) and stage-wise subtractive block (SSB). Second, feature corrector stream (FCS) mainly focuses on refining the partial outputs of the first stream, reducing the veiling effect and acts supplementary to the first stream. Finally, we leverage contrastive regularization for better convergence of the proposed network. Substantial experiments on synthetic as well as real-world images, along with extensive ablation studies, demonstrate that the proposed method performs competitively with the existing methods for multi-weather image restoration. The code is available at: https://***/AshutoshKulkarni4998/UVRNet.

关键词： Lightweight degradation removal multi-weather restoration surveillance applications

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Soft set-based MSER end-to-end system for occluded scene text detection, recognition and prediction

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Das, Alloy Palaiahnakote, Shivakumara Banerjee, Ayan Antonacopoulos, Apostolos Pal, Umapada Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata India Univ Salford Pattern Recognit & Image Anal PRImA Res Lab Manchester England

The presence of unpredictable occlusions on natural scene text is a significant challenge, exacerbating the difficulties already posed on text detection and recognition by the variability of such images. Addressing the need for a robust, consistently performing approach that can effectively address the above challenges, this paper presents a new Soft Set-based end-to-end system for text detection, recognition and prediction in occluded natural scene images. This is the first approach to integrate text detection, recognition and prediction, unlike existing systems developed for end-to-end text spotting (text detection and recognition) only. For candidate text components detection, the proposed combination of Soft Sets with Maximally Stable Extremal Regions (SSMSER) improves text detection and spotting in natural scene images, irrespectively of the presence of arbitrarily orientated and shaped text, complex backgrounds and occlusion. Furthermore, a Graph Recurrent Neural Network is proposed for grouping candidate text components into text lines and for fitting accurate bounding boxes to each word. Finally, a Convolutional Recurrent Neural Network (CRNN) is proposed for the recognition of text and for predicting missing characters due to occlusion. Experimental results on a new occluded scene text dataset (OSTD) and on the most relevant benchmark natural scene text datasets demonstrate that the proposed system outperforms the state-of-the-art in text detection, recognition and prediction. The code and dataset are available at https://***/alloydas/Softset-MSER-Based-Occluded-Scene-Text-Spotting/blob/master/S oft_set_***

关键词： Scene text detection Scene text recognition Scene text correction Occluded scene text Graph neural network Convolutional recurrent neural network Convolutional neural network

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NORPPA: NOvel Ringed seal re-identification by Pelage pattern Aggregation

NORPPA: NOvel Ringed seal re-identification by Pelage Patter...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Nepovinnykh, Ekaterina Eerola, Tuomas Kalviainen, Heikki Chelak, Ilia Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Dept Computat Engn Comp Vis & Pattern Recognit Lab CVPRL Lappeenranta Finland Univ Helsinki Dept Comp Sci Fac Sci Helsinki Finland

ISBN: (纸本)9798350370287;9798350370713

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

关键词： Animals

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HD-Net: A hybrid dynamic spatio-temporal network for traffic flow prediction

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IET INTELLIGENT TRANSPORT SYSTEMS 2024年第4期18卷 672-690页

作者： Liu, Lijuan Wang, Fengzhi Liu, Hang Zhu, Shunzhi Wang, Yan Xiamen Univ Technol Coll Comp & Informat Engn Xiamen Peoples R China Fujian Key Lab Pattern Recognit & Image Understand Xiamen Peoples R China Xiamen Univ Technol Coll Comp & Informat Engn Xiamen 361024 Peoples R China

Accurately predicting traffic flow is crucial for intelligent transportation systems (ITS). In recent years, many deep learning-based prediction models have been widely applied in traffic flow prediction, and various spatio-temporal networks have been proposed. However, most of the existing models follow a general technical route to extract the spatio-temporal features, which lack the capacity of extracting the important historical information with the high spatial and temporal correlations dynamically and deeply. How to develop a well-performance traffic flow prediction model for a complex transportation network is still facing some challenges. In this paper, a hybrid dynamic spatio-temporal network (HD-Net) for traffic flow prediction is proposed. In HD-Net, the authors first extract the dynamic spatio-temporal features using dynamic graph convolution and bidirectional gate recurrent uni (BiGRU). Subsequently, the authors extract the important features with high spatial and temporal correlations from the obtained dynamic spatio-temporal features using an auto-correlation mechanism from a local perspective, and self-attention mechanism from a global perspective, respectively. Extensive experiments have been conducted on two real-world traffic datasets. The experimental results demonstrate that the proposed HD-Net outperforms the baselines in the field of capturing the dynamic and important spatio-temporal features with high correlations. In HD-Net, the authors first extract the dynamic spatio-temporal features using dynamic graph convolution and bidirectional gate recurrent unit (BiGRU). Then, the authors further extract the important features with high spatial and temporal correlation from the obtained dynamic spatio-temporal features using the auto-correlation mechanism from a local perspective, and self-attention mechanism from a global ***

关键词： management and control traffic modelling transport modelling and microsimulation

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