International Journal on Document Analysis and Recognition最新文献

{"title":"An unsupervised automatic organization method for Professor Shirakawa’s hand-notated documents of oracle bone inscriptions","authors":"Xuebin Yue, Ziming Wang, Ryuto Ishibashi, Hayata Kaneko, Lin Meng","doi":"10.1007/s10032-024-00463-0","DOIUrl":"https://doi.org/10.1007/s10032-024-00463-0","url":null,"abstract":"<p>As one of the most influential Chinese cultural researchers in the second half of the twentieth-century, Professor Shirakawa is active in the research field of ancient Chinese characters. He has left behind many valuable research documents, especially his hand-notated oracle bone inscriptions (OBIs) documents. OBIs are one of the world’s oldest characters and were used in the Shang Dynasty about 3600 years ago for divination and recording events. The organization of OBIs is not only helpful in better understanding Prof. Shirakawa’s research and further study of OBIs in general and their importance in ancient Chinese history. This paper proposes an unsupervised automatic organization method to organize Prof. Shirakawa’s OBIs and construct a handwritten OBIs data set for neural network learning. First, a suite of noise reduction is proposed to remove strangely shaped noise to reduce the data loss of OBIs. Secondly, a novel segmentation method based on the supervised classification of OBIs regions is proposed to reduce adverse effects between characters for more accurate OBIs segmentation. Thirdly, a unique unsupervised clustering method is proposed to classify the segmented characters. Finally, all the same characters in the hand-notated OBIs documents are organized together. The evaluation results show that noise reduction has been proposed to remove noises with an accuracy of 97.85%, which contains number information and closed-loop-like edges in the dataset. In addition, the accuracy of supervised classification of OBIs regions based on our model achieves 85.50%, which is higher than eight state-of-the-art deep learning models, and a particular preprocessing method we proposed improves the classification accuracy by nearly 11.50%. The accuracy of OBIs clustering based on supervised classification achieves 74.91%. These results demonstrate the effectiveness of our proposed unsupervised automatic organization of Prof. Shirakawa’s hand-notated OBIs documents. The code and datasets are available at http://www.ihpc.se.ritsumei.ac.jp/obidataset.html.</p>","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"101 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140046535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the improvement of handwritten text line recognition with octave convolutional recurrent neural networks","authors":"Dayvid Castro, Cleber Zanchettin, Luís A. Nunes Amaral","doi":"10.1007/s10032-024-00460-3","DOIUrl":"https://doi.org/10.1007/s10032-024-00460-3","url":null,"abstract":"<p>Off-line handwritten text recognition (HTR) poses a significant challenge due to the complexities of variable handwriting styles, background degradation, and unconstrained word sequences. This work tackles the handwritten text line recognition problem using octave convolutional recurrent neural networks (OctCRNN). Our approach requires no word segmentation, preprocessing, or explicit feature extraction and leverages octave convolutions to process multiscale features without increasing the number of learnable parameters. We investigate the OctCRNN under different settings, including an octave design that efficiently balances computational cost and recognition performance. We thoroughly investigate the OctCRNN under different settings by formulating an experimental pipeline with a visualization step to get intuitions about how the model works compared to a counterpart based on traditional convolutions. The system becomes complete by adding a language model to increase linguistic knowledge. Finally, we assess the performance of our solution using character and word error rates against established handwritten text recognition benchmarks: IAM, RIMES, and ICFHR 2016 READ. According to the results, our proposal achieves state-of-the-art performance while reducing the computational requirements. Our findings suggest that the architecture provides a robust framework for building HTR systems.</p>","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"3 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139911073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Training transformer architectures on few annotated data: an application to historical handwritten text recognition","authors":"Killian Barrere, Yann Soullard, Aurélie Lemaitre, Bertrand Coüasnon","doi":"10.1007/s10032-023-00459-2","DOIUrl":"https://doi.org/10.1007/s10032-023-00459-2","url":null,"abstract":"<p>Transformer-based architectures show excellent results on the task of handwritten text recognition, becoming the standard architecture for modern datasets. However, they require a significant amount of annotated data to achieve competitive results. They typically rely on synthetic data to solve this problem. Historical handwritten text recognition represents a challenging task due to degradations, specific handwritings for which few examples are available and ancient languages that vary over time. These limitations also make it difficult to generate realistic synthetic data. Given sufficient and appropriate data, Transformer-based architectures could alleviate these concerns, thanks to their ability to have a global view of textual images and their language modeling capabilities. In this paper, we propose the use of a lightweight Transformer model to tackle the task of historical handwritten text recognition. To train the architecture, we introduce realistic looking synthetic data reproducing the style of historical handwritings. We present a specific strategy, both for training and prediction, to deal with historical documents, where only a limited amount of training data are available. We evaluate our approach on the ICFHR 2018 READ dataset which is dedicated to handwriting recognition in specific historical documents. The results show that our Transformer-based approach is able to outperform existing methods.</p>","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"26 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139580505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Background grid extraction from historical hand-drawn cadastral maps","authors":"Tauseef Iftikhar, Nazar Khan","doi":"10.1007/s10032-023-00457-4","DOIUrl":"https://doi.org/10.1007/s10032-023-00457-4","url":null,"abstract":"<p>We tackle a novel problem of detecting background grids in hand-drawn cadastral maps. Grid extraction is necessary for accessing and contextualizing the actual map content. The problem is challenging since the background grid is the bottommost map layer that is severely occluded by subsequent map layers. We present a novel automatic method for robust, bottom-up extraction of background grid structures in historical cadastral maps. The proposed algorithm extracts grid structures under significant occlusion, missing information, and noise by iteratively providing an increasingly refined estimate of the grid structure. The key idea is to exploit periodicity of background grid lines to corroborate the existence of each other. We also present an automatic scheme for determining the ‘gridness’ of any detected grid so that the proposed method self-evaluates its result as being good or poor without using ground truth. We present empirical evidence to show that the proposed gridness measure is a good indicator of quality. On a dataset of 268 historical cadastral maps with resolution <span>(1424times 2136)</span> pixels, the proposed method detects grids in 247 images yielding an average root-mean-square error (RMSE) of 5.0 pixels and average intersection over union (IoU) of 0.990. On grids self-evaluated as being good, we report average RMSE of 4.39 pixels and average IoU of 0.991. To compare with the proposed bottom-up approach, we also develop three increasingly sophisticated top-down algorithms based on RANSAC-based model fitting. Experimental results show that our bottom-up algorithm yields better results than the top-down algorithms. We also demonstrate that using detected background grids for stitching different maps is visually better than both manual and SURF-based stitching.</p>","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138556858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genre as noise","authors":"StubbeAndrea, RinglstetterChristoph, U. SchulzKlaus","doi":"10.2307/j.ctv125jncf.8","DOIUrl":"https://doi.org/10.2307/j.ctv125jncf.8","url":null,"abstract":"Given a specific information need, documents of the wrong genre can be considered as noise. From this perspective, genre classification helps to separate relevant documents from noise. Orthographic...","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"15 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84556320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text line segmentation of historical documents: a survey","authors":"Likforman-SulemLaurence, ZahourAbderrazak, TaconetBruno","doi":"10.5555/1237480.1237483","DOIUrl":"https://doi.org/10.5555/1237480.1237483","url":null,"abstract":"There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in mo...","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"43 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2007-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85190118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text line segmentation of historical documents","authors":"Likforman-SulemLaurence, ZahourAbderrazak, TaconetBruno","doi":"10.5555/2722890.2723025","DOIUrl":"https://doi.org/10.5555/2722890.2723025","url":null,"abstract":"There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in mo...","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"77 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85564508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The recognition of handwritten numeral strings using a two-stage HMM-based method","authors":"A. Britto, R. Sabourin, Flávio Bortolozzi","doi":"10.1007/s10032-002-0085-5","DOIUrl":"https://doi.org/10.1007/s10032-002-0085-5","url":null,"abstract":"","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"7 1","pages":"102-117"},"PeriodicalIF":2.3,"publicationDate":"2003-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89695619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive image-smoothing using a coplanar matrix and its application to document image binarization","authors":"Lixin Fan, Liying Fan, C. Tan","doi":"10.1007/s10032-002-0098-0","DOIUrl":"https://doi.org/10.1007/s10032-002-0098-0","url":null,"abstract":"","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"262 1","pages":"88-101"},"PeriodicalIF":2.3,"publicationDate":"2003-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82471177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special issue – selected papers from the ICDAR'01 conference","authors":"A. Spitz, K. Tombre","doi":"10.1007/s10032-002-0093-5","DOIUrl":"https://doi.org/10.1007/s10032-002-0093-5","url":null,"abstract":"","PeriodicalId":50277,"journal":{"name":"International Journal on Document Analysis and Recognition","volume":"25 1","pages":"87"},"PeriodicalIF":2.3,"publicationDate":"2003-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82836000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}