In this paper, we approach the problem of segmentation-free query-by-string word spotting for handwritten documents. In other words, we use methods inspired from computer vision and machine learning to search for words in large collections of digitized manuscripts. In particular, we are interested in historical handwritten texts, which are often far more challenging than modern printed documents. This task is important, as it provides people with a way to quickly find what they are looking for in large collections that are tedious and difficult to read manually. To this end, we introduce an end-to-end trainable model based on deep neural networks that we call Ctrl-F-Net. Given a full manuscript page, the model simultaneously generates region proposals, and embeds these into a distributed word embedding space, where searches are performed. We evaluate the model on common benchmarks for handwritten word spotting, outperforming the previous state-of-the-art segmentation-free approaches by a large margin, and in some cases even segmentation-based approaches. One interesting real-life application of our approach is to help historians to find and count specific words in court records that are related to women's sustenance activities and division of labor. We provide promising preliminary experiments that validate our method on this task.
The second category consists of methods based on connected components @cite_27 @cite_29 . These methods are typically based on binarizing the manuscript image, extracting connected components, and then grouping them in a bottom-up fashion following some heuristics, and then extracting bounding boxes. A similar approach is used in @cite_48 for matching entire documents using distributions of word images. While still producing too many regions, they are fewer compared to methods based on the sliding window and they are not sensitive to shifts in the input to the same extent.
@cite_48 @cite_27 @cite_29
2166347079 2022303741 2148645152 2962984063
This paper describes new methods of automatically extracting documents for screening purposes, i.e. the computer selection of sentences having the greatest potential for conveying to the reader the substance of the document. While previous work has focused on one component of sentence significance, namely, the presence of high-frequency content words (key words), the methods described here also treat three additional components: pragmatic words (cue words); title and heading words; and structural indicators (sentence location). The research has resulted in an operating system and a research methodology. The extracting system is parameterized to control and vary the influence of the above four components. The research methodology includes procedures for the compilation of the required dictionaries, the setting of the control parameters, and the comparative evaluation of the automatic extracts with manually produced extracts. The results indicate that the three newly proposed components dominate the frequency component in the production of better extracts. This paper presents an efficient approach for copies detection in a large videos archive consisting of several hundred of hours. The video content indexing method consists of extracting the dynamic behavior on the local description of interest points and further on the estimation of their trajectories along the video sequence. Analyzing the low-level description obtained allows to highlight trends of behaviors and then to assign a label of behavior to each local descriptor. Such an indexing approach has several interesting properties: it provides a rich, compact and generic description, while labels of behavior provide a high-level description of the video content. Here, we focus on video Content Based Copy Detection (CBCD). Copy detection is problematic as similarity search problem but with prominent differences. To be efficient, it requires a dedicated on-line retrieval method based on a specific voting function. This voting function must be robust to signal transformations and discriminating versus high similarities which are not copies. The method we propose in this paper is a dedicated on-line retrieval method based on a combination of the different dynamic contexts computed during the off-line indexing. A spatio-temporal registration based on the relevant combination of detected labels is then applied. This approach is evaluated using a huge video database of 300 hours with different video tests. The method is compared to a state-of-the art technique in the same conditions. We illustrate that taking labels into account in the specific voting process reduces false alarms significantly and drastically improves the precision. In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists of three steps. Firstly, a steerable filter is used to probe and determine foreground intensity along multiple directions at each pixel while generating the ALCM. The ALCM is then binarized using an adaptive thresholding algorithm to get a rough estimate of the location of the text lines. In the second step, connected component analysis is used to classify text and non text patterns in the generated ALCM to refine the location of the text lines. Finally, the text lines are separated by superimposing the text line patterns in the ALCM on the original document image and extracting the connected components covered by the pattern mask. Analysis of experimental results on the DARPA MADCAT Arabic handwritten document data indicate that the method is robust and is capable of correctly isolating handwritten text lines even on challenging document images. Abstract Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (, 2013; , 2014; , 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way. Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (, 2014; , 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 F -score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https: github.com lluisgomez TextProposals .