Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00107
Zelin Hong, Ning You, J. Tan, Ning Bi
In this paper, we present a Seq2Seq model for online handwritten mathematical expression recognition (OHMER), which consists of two major parts: a residual bidirectional RNN (BiRNN) based encoder that takes handwritten traces as the input and a transition probability matrix introduced decoder that generates LaTeX notations. We employ residual connection in the BiRNN layers to improve feature extraction. Markovian transition probability matrix is introduced in decoder and long-term information can be used in each decoding step through joint probability. Furthermore, we analyze the impact of the novel encoder and transition probability matrix through several specific instances. Experimental results on the CROHME 2014 and CROHME 2016 competition tasks show that our model outperforms the previous state-of-the-art single model by only using the official training dataset.
{"title":"Residual BiRNN Based Seq2Seq Model with Transition Probability Matrix for Online Handwritten Mathematical Expression Recognition","authors":"Zelin Hong, Ning You, J. Tan, Ning Bi","doi":"10.1109/ICDAR.2019.00107","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00107","url":null,"abstract":"In this paper, we present a Seq2Seq model for online handwritten mathematical expression recognition (OHMER), which consists of two major parts: a residual bidirectional RNN (BiRNN) based encoder that takes handwritten traces as the input and a transition probability matrix introduced decoder that generates LaTeX notations. We employ residual connection in the BiRNN layers to improve feature extraction. Markovian transition probability matrix is introduced in decoder and long-term information can be used in each decoding step through joint probability. Furthermore, we analyze the impact of the novel encoder and transition probability matrix through several specific instances. Experimental results on the CROHME 2014 and CROHME 2016 competition tasks show that our model outperforms the previous state-of-the-art single model by only using the official training dataset.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133622445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00222
Tonghua Su, Wei Pan, Lijuan Yu
Current state of handwritten Chinese character recognition (HCCR) conducted on well-confined character set, far from meeting industrial requirements. The paper describes the creation of a large-scale handwritten Chinese character database. Constructing the database is an effort to scale up Chinese handwritten character classification task to cover the full list of GBK character set specification. It consists of 21-thousand Chinese character categories and 20-million character images, larger than previous databases both in scale and diversity. We present solutions to the challenges of collecting and annotating such large-scale handwritten character samples. We elaborately design the sampling strategy, extract salient signals in a systematic way, annotate the tremendous characters through three distinct stages. Experiments are conducted the generalization to other handwritten character databases and our database demonstrates great values. Surely, its scale opens unprecedented opportunities both in evaluation of character recognition algorithms and in developing new techniques.
{"title":"HITHCD-2018: Handwritten Chinese Character Database of 21K-Category","authors":"Tonghua Su, Wei Pan, Lijuan Yu","doi":"10.1109/ICDAR.2019.00222","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00222","url":null,"abstract":"Current state of handwritten Chinese character recognition (HCCR) conducted on well-confined character set, far from meeting industrial requirements. The paper describes the creation of a large-scale handwritten Chinese character database. Constructing the database is an effort to scale up Chinese handwritten character classification task to cover the full list of GBK character set specification. It consists of 21-thousand Chinese character categories and 20-million character images, larger than previous databases both in scale and diversity. We present solutions to the challenges of collecting and annotating such large-scale handwritten character samples. We elaborately design the sampling strategy, extract salient signals in a systematic way, annotate the tremendous characters through three distinct stages. Experiments are conducted the generalization to other handwritten character databases and our database demonstrates great values. Surely, its scale opens unprecedented opportunities both in evaluation of character recognition algorithms and in developing new techniques.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130339133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00011
Chris Tensmeyer, Curtis Wigington
Training Handwritten Text Recognition (HTR) models typically requires large amounts of labeled data which often are line or page images with corresponding line-level ground truth (GT) transcriptions. Many digital collections have page-level transcriptions for each image, but the transcription is unformatted, i.e., line breaks are not annotated. Can we train lined-based HTR models using such data? In this work, we present a novel alignment technique for segmenting page-level GT text into text lines during HTR model training. This text segmentation problem is formulated as an optimization problem to minimize the cost of aligning predicted lines with the GT text. Using both simulated and HTR model predictions, we show that the alignment method identifies line breaks accurately, even when the predicted lines have high character error rates (CER). We removed the GT line breaks from the ICDAR-2017 READ dataset and trained a HTR model using the proposed alignment method to predict line breaks on-the-fly. This model achieves comparable CER w.r.t. to the same model trained with the GT line breaks. Additionally, we downloaded an online digital collection of 50K English journal pages (not curated for HTR research) whose transcriptions do not contain line breaks, and achieve 11% CER.
{"title":"Training Full-Page Handwritten Text Recognition Models without Annotated Line Breaks","authors":"Chris Tensmeyer, Curtis Wigington","doi":"10.1109/ICDAR.2019.00011","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00011","url":null,"abstract":"Training Handwritten Text Recognition (HTR) models typically requires large amounts of labeled data which often are line or page images with corresponding line-level ground truth (GT) transcriptions. Many digital collections have page-level transcriptions for each image, but the transcription is unformatted, i.e., line breaks are not annotated. Can we train lined-based HTR models using such data? In this work, we present a novel alignment technique for segmenting page-level GT text into text lines during HTR model training. This text segmentation problem is formulated as an optimization problem to minimize the cost of aligning predicted lines with the GT text. Using both simulated and HTR model predictions, we show that the alignment method identifies line breaks accurately, even when the predicted lines have high character error rates (CER). We removed the GT line breaks from the ICDAR-2017 READ dataset and trained a HTR model using the proposed alignment method to predict line breaks on-the-fly. This model achieves comparable CER w.r.t. to the same model trained with the GT line breaks. Additionally, we downloaded an online digital collection of 50K English journal pages (not curated for HTR research) whose transcriptions do not contain line breaks, and achieve 11% CER.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131560494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/icdar.2019.00025
Najah-Imane Bentabet, Rémi Juge, Sira Ferradans
The generation of precise and detailed Table-Of-Contents (TOC) from a document is a problem of major importance for document understanding and information extraction. Despite its importance, it is still a challenging task, especially for non-standardized documents with rich layout information such as commercial documents. In this paper, we present a new neural-based pipeline for TOC generation applicable to any searchable document. Unlike previous methods, we do not use semantic labeling nor assume the presence of parsable TOC pages in the document. Moreover, we analyze the influence of using external knowledge encoded as a template. We empirically show that this approach is only useful in a very low resource environment. Finally, we propose a new domain-specific data set that sheds some light on the difficulties of TOC generation in real-world documents. The proposed method shows better performance than the state-of-the-art on a public data set and on the newly released data set.
{"title":"Table-of-Contents Generation on Contemporary Documents","authors":"Najah-Imane Bentabet, Rémi Juge, Sira Ferradans","doi":"10.1109/icdar.2019.00025","DOIUrl":"https://doi.org/10.1109/icdar.2019.00025","url":null,"abstract":"The generation of precise and detailed Table-Of-Contents (TOC) from a document is a problem of major importance for document understanding and information extraction. Despite its importance, it is still a challenging task, especially for non-standardized documents with rich layout information such as commercial documents. In this paper, we present a new neural-based pipeline for TOC generation applicable to any searchable document. Unlike previous methods, we do not use semantic labeling nor assume the presence of parsable TOC pages in the document. Moreover, we analyze the influence of using external knowledge encoded as a template. We empirically show that this approach is only useful in a very low resource environment. Finally, we propose a new domain-specific data set that sheds some light on the difficulties of TOC generation in real-world documents. The proposed method shows better performance than the state-of-the-art on a public data set and on the newly released data set.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132803367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00088
Xiaohui Li, Fei Yin, Tao Xue, Long Liu, J. Ogier, Cheng-Lin Liu
Segmentation of complex document images remains a challenge due to the large variability of layout and image degradation. In this paper, we propose a method to segment complex document images based on Label Pyramid Network (LPN) and Deep Watershed Transform (DWT). The method can segment document images into instance aware regions including text lines, text regions, figures, tables, etc. The backbone of LPN can be any type of Fully Convolutional Networks (FCN), and in training, label map pyramids on training images are provided to exploit the hierarchical boundary information of regions efficiently through multi-task learning. The label map pyramid is transformed from region class label map by distance transformation and multi-level thresholding. In segmentation, the outputs of multiple tasks of LPN are summed into one single probability map, on which watershed transformation is carried out to segment the document image into instance aware regions. In experiments on four public databases, our method is demonstrated effective and superior, yielding state of the art performance for text line segmentation, baseline detection and region segmentation.
{"title":"Instance Aware Document Image Segmentation using Label Pyramid Networks and Deep Watershed Transformation","authors":"Xiaohui Li, Fei Yin, Tao Xue, Long Liu, J. Ogier, Cheng-Lin Liu","doi":"10.1109/ICDAR.2019.00088","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00088","url":null,"abstract":"Segmentation of complex document images remains a challenge due to the large variability of layout and image degradation. In this paper, we propose a method to segment complex document images based on Label Pyramid Network (LPN) and Deep Watershed Transform (DWT). The method can segment document images into instance aware regions including text lines, text regions, figures, tables, etc. The backbone of LPN can be any type of Fully Convolutional Networks (FCN), and in training, label map pyramids on training images are provided to exploit the hierarchical boundary information of regions efficiently through multi-task learning. The label map pyramid is transformed from region class label map by distance transformation and multi-level thresholding. In segmentation, the outputs of multiple tasks of LPN are summed into one single probability map, on which watershed transformation is carried out to segment the document image into instance aware regions. In experiments on four public databases, our method is demonstrated effective and superior, yielding state of the art performance for text line segmentation, baseline detection and region segmentation.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134315883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00024
Julien Maître, M. Ménard, Guillaume Chiron, A. Bouju, Nicolas Sidère
This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multi-dimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the "a priori" number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a non-traditional visualization approach based on a multi-agents system which combines both dimension reduction and interactivity.
{"title":"A Meaningful Information Extraction System for Interactive Analysis of Documents","authors":"Julien Maître, M. Ménard, Guillaume Chiron, A. Bouju, Nicolas Sidère","doi":"10.1109/ICDAR.2019.00024","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00024","url":null,"abstract":"This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multi-dimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the \"a priori\" number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a non-traditional visualization approach based on a multi-agents system which combines both dimension reduction and interactivity.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133812088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00185
Rubén Tolosana, R. Vera-Rodríguez, Julian Fierrez, A. Morales, J. Ortega-Garcia
Data have become one of the most valuable things in this new era where deep learning technology seems to overcome traditional approaches. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel approaches compared with the state of the art as different experimental protocols and conditions are usually considered for different signature databases. To tackle all these mentioned problems, the main contribution of this study is twofold: i) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, and ii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art. The DeepSignDB database is obtained through the combination of some of the most popular on-line signature databases, and a novel dataset not presented yet. It comprises more than 70K signatures acquired using both stylus and finger inputs from a total of 1526 users. Two acquisition scenarios are considered, office and mobile, with a total of 8 different devices. Additionally, different types of impostors and number of acquisition sessions are considered along the database. The DeepSignDB and benchmark results are available in GitHub.
{"title":"Do You Need More Data? The DeepSignDB On-Line Handwritten Signature Biometric Database","authors":"Rubén Tolosana, R. Vera-Rodríguez, Julian Fierrez, A. Morales, J. Ortega-Garcia","doi":"10.1109/ICDAR.2019.00185","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00185","url":null,"abstract":"Data have become one of the most valuable things in this new era where deep learning technology seems to overcome traditional approaches. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel approaches compared with the state of the art as different experimental protocols and conditions are usually considered for different signature databases. To tackle all these mentioned problems, the main contribution of this study is twofold: i) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, and ii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art. The DeepSignDB database is obtained through the combination of some of the most popular on-line signature databases, and a novel dataset not presented yet. It comprises more than 70K signatures acquired using both stylus and finger inputs from a total of 1526 users. Two acquisition scenarios are considered, office and mobile, with a total of 8 different devices. Additionally, different types of impostors and number of acquisition sessions are considered along the database. The DeepSignDB and benchmark results are available in GitHub.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123879340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00225
Shoaib Ahmed Siddiqui, Pervaiz Iqbal Khan, A. Dengel, Sheraz Ahmed
Based on the recent advancements in the domain of semantic segmentation, Fully-Convolutional Networks (FCN) have been successfully applied for the task of table structure recognition in the past. We analyze the efficacy of semantic segmentation networks for this purpose and simplify the problem by proposing prediction tiling based on the consistency assumption which holds for tabular structures. For an image of dimensions H × W, we predict a single column for the rows (ŷ_row ∊ H) and a predict a single row for the columns (ŷ_row ∊ W). We use a dual-headed architecture where initial feature maps (from the encoder-decoder model) are shared while the last two layers generate class specific (row/column) predictions. This allows us to generate predictions using a single model for both rows and columns simultaneously, where previous methods relied on two separate models for inference. With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13 image-based table structure recognition dataset with an average F-Measure of 92.39% (91.90% and 92.88% F-Measure for rows and columns respectively). With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13. The obtained results advocate that constraining the problem space in the case of FCN by imposing valid constraints can lead to significant performance gains.
{"title":"Rethinking Semantic Segmentation for Table Structure Recognition in Documents","authors":"Shoaib Ahmed Siddiqui, Pervaiz Iqbal Khan, A. Dengel, Sheraz Ahmed","doi":"10.1109/ICDAR.2019.00225","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00225","url":null,"abstract":"Based on the recent advancements in the domain of semantic segmentation, Fully-Convolutional Networks (FCN) have been successfully applied for the task of table structure recognition in the past. We analyze the efficacy of semantic segmentation networks for this purpose and simplify the problem by proposing prediction tiling based on the consistency assumption which holds for tabular structures. For an image of dimensions H × W, we predict a single column for the rows (ŷ_row ∊ H) and a predict a single row for the columns (ŷ_row ∊ W). We use a dual-headed architecture where initial feature maps (from the encoder-decoder model) are shared while the last two layers generate class specific (row/column) predictions. This allows us to generate predictions using a single model for both rows and columns simultaneously, where previous methods relied on two separate models for inference. With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13 image-based table structure recognition dataset with an average F-Measure of 92.39% (91.90% and 92.88% F-Measure for rows and columns respectively). With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13. The obtained results advocate that constraining the problem space in the case of FCN by imposing valid constraints can lead to significant performance gains.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124011225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00155
Momina Moetesum, I. Siddiqi, N. Vincent
Sketches and drawings are popularly employed in clinical psychology to assess the visual-motor and perceptual development in children and adolescents. Drawn responses by subjects are mostly characterized by high degree of deformations that indicates presence of various visual, perceptual and motor disorders. Classification of deformations is a challenging task due to complex and extensive rule representation. In this study, we propose a novel technique to model clinical manifestations using Deep Convolutional Neural Networks (DCNNs). Drawn responses of nine templates used for assessment of perceptual orientation of individuals are employed as training samples. A number of defined deviations scored in each template are then modeled by applying fine tuning on a pre-trained DCNN architecture. Performance of the proposed technique is evaluated on samples of 106 children. Results of experiments show that pre-trained DCNNs can model and classify a number of deformations across multiple shapes with considerable success. Nevertheless some deformations are represented more reliably than the others. Overall promising classification results are observed that substantiate the effectiveness of our proposed technique.
{"title":"Deformation Classification of Drawings for Assessment of Visual-Motor Perceptual Maturity","authors":"Momina Moetesum, I. Siddiqi, N. Vincent","doi":"10.1109/ICDAR.2019.00155","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00155","url":null,"abstract":"Sketches and drawings are popularly employed in clinical psychology to assess the visual-motor and perceptual development in children and adolescents. Drawn responses by subjects are mostly characterized by high degree of deformations that indicates presence of various visual, perceptual and motor disorders. Classification of deformations is a challenging task due to complex and extensive rule representation. In this study, we propose a novel technique to model clinical manifestations using Deep Convolutional Neural Networks (DCNNs). Drawn responses of nine templates used for assessment of perceptual orientation of individuals are employed as training samples. A number of defined deviations scored in each template are then modeled by applying fine tuning on a pre-trained DCNN architecture. Performance of the proposed technique is evaluated on samples of 106 children. Results of experiments show that pre-trained DCNNs can model and classify a number of deformations across multiple shapes with considerable success. Nevertheless some deformations are represented more reliably than the others. Overall promising classification results are observed that substantiate the effectiveness of our proposed technique.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128824004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/ICDAR.2019.00051
Romain Karpinski, A. Belaïd
This paper proposes a fully automatic new method for generating semi-synthetic images of historical documents to increase the number of training samples in small datasets. This method extracts and mixes background only images (BOI) with text only images (TOI) issued from two different sources to create semi-synthetic images. The TOIs are extracted with the help of a binary mask obtained by binarizing the image. The BOIs are reconstructed from the original image by replacing TOI pixels using an inpainting method. Finally, a TOI can be efficiently integrated in a BOI using the gradient domain, thus creating a new semi-synthetic image. The idea behind this technique is to automatically obtain documents close to real ones with different backgrounds to highlight the content. Experiments are conducted on the public HisDB dataset which contains few labeled images. We show that the proposed method improves the performance results of a semantic segmentation and baseline extraction task.
{"title":"Semi-Synthetic Data Augmentation of Scanned Historical Documents","authors":"Romain Karpinski, A. Belaïd","doi":"10.1109/ICDAR.2019.00051","DOIUrl":"https://doi.org/10.1109/ICDAR.2019.00051","url":null,"abstract":"This paper proposes a fully automatic new method for generating semi-synthetic images of historical documents to increase the number of training samples in small datasets. This method extracts and mixes background only images (BOI) with text only images (TOI) issued from two different sources to create semi-synthetic images. The TOIs are extracted with the help of a binary mask obtained by binarizing the image. The BOIs are reconstructed from the original image by replacing TOI pixels using an inpainting method. Finally, a TOI can be efficiently integrated in a BOI using the gradient domain, thus creating a new semi-synthetic image. The idea behind this technique is to automatically obtain documents close to real ones with different backgrounds to highlight the content. Experiments are conducted on the public HisDB dataset which contains few labeled images. We show that the proposed method improves the performance results of a semantic segmentation and baseline extraction task.","PeriodicalId":325437,"journal":{"name":"2019 International Conference on Document Analysis and Recognition (ICDAR)","volume":"21 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116374623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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