Historical Handwritten Text Images Word Spotting through Sliding Window HOG Features

In this paper we present an innovative technique to semi-automatically index handwritten word images. The proposed method is based on HOG descriptors and exploits Dynamic Time Warping technique to compare feature vectors elaborated from single handwritten words. Our strategy is applied to a new challenging dataset extracted from Italian civil registries of the XIX century. Experimental results, compared with some previously developed word spotting strategies, confirmed that our method outperforms competitors

Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques

One of the most important steps of document image processing is binarization. The computational requirements of locally adaptive binarization techniques make them unsuitable for devices with limited computing facilities. In this paper, we have presented a computationally efficient implementation of convolution based locally adaptive binarization techniques keeping the performance comparable to the original implementation. The computational complexity has been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the image size. Experiments over benchmark datasets show that the computation time has been reduced by 5 to 15 times depending on the window size while memory consumption remains the same with respect to the state-of-the-art algorithmic implementation

On the modification of binarization algorithms to retain grayscale information for handwritten text recognition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_24[EN] The amount of digitized legacy documents has been rising over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents. The vast majority of them remain waiting to be transcribed to provide historians and other researchers new ways of indexing, consulting and querying them. However, the performance accuracy of state-of-the-art Handwritten Text Recognition techniques decreases dramatically when they are applied to these historical documents. This is mainly due to the typical paper degradation problems. Therefore, robust pre-processing techniques is an important step for helping further recognition steps. This paper proposes to take existing binarization techniques, in order to retain their advantages, and modify them in such a way that some of the original grayscale information is preserved and be considered by the subsequent recognizer. Results are reported with the publicly available ESPOSALLES database.The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7/2007-2013) under grant agreement No. 600707 - tranScriptorium and the Spanish MEC under the STraDA project (TIN2012-37475-C02-01).Villegas, M.; Romero Gómez, V.; Sánchez Peiró, JA. (2015). On the modification of binarization algorithms to retain grayscale information for handwritten text recognition. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 208-215. https://doi.org/10.1007/978-3-319-19390-8_24S208215Drida, F.: Towards restoring historic documents degraded over time. In: Proceedings of 2nd IEEE International Conference on Document Image Analysis for Libraries (DIAL 2006), Lyon, France, pp. 350–357 (2006)Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)Khurshid, K., Siddiqi, I., Faure, C., Vincent, N.: Comparison of niblack inspired binarization methods for ancient documents. In: Berkner, K., Likforman-Sulem, L. (eds.) 16th Document Recognition and Retrieval Conference, DRR 2009, SPIE Proceedings, vol. 7247, pp. 1–10. SPIE, San Jose (18–22 January 2009). doi: 10.1117/12.805827Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling, Detroit, USA, vol. 1, pp. 181–184 (1995)Marti, U., Bunke, H.: Using a statistical language model to improve the preformance of an HMM-based cursive handwriting recognition system. IJPRAI 15(1), 65–90 (2001)Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Prentice-Hall, Englewood Cliffs (1986)Romero, V., Fornés, A., Serrano, N., Sánchez, J., Toselli, A., Frinken, V., Vidal, E., Lladós, J.: The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition. Pattern Recogn. 46, 1658–1669 (2013). doi: 10.1016/j.patcog.2012.11.024España-Boquera, S., Castro-Bleda, M.J., Gorbe-Moya, J., Zamora-Martínez, F.: Improving offline handwriting text recognition with hybrid hmm/ann models. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 767–779 (2011)Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recog. 33(2), 225–236 (2000). doi: 10.1016/S0031-3203(99)00055-2Shafait, F., Keysers, D., Breuel, T.M.: Efficient implementation of local adaptive thresholding techniques using integral images. In: Proceedings of the SPIE 6815, Document Recognition and Retrieval XV, 681510, pp. 1–6, January 2008. doi: 10.1117/12.767755Toselli, A.H., Juan, A., Keysers, D., González, J., Salvador, I., Ney, H., Vidal, E., Casacuberta, F.: Integrated handwriting recognition and interpretation using finite-state models. Int. J. Pattern Recog. Artif. Intell. 18(4), 519–539 (2004). doi: 10.1142/S021800140400334

A Generalization of Otsu's Method and Minimum Error Thresholding

We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.Comment: ECCV 202

Improving the Performance of Thinning Algorithms with Directed Rooted Acyclic Graphs

In this paper we propose a strategy to optimize the performance of thinning algorithms. This solution is obtained by combining three proven strategies for binary images neighborhood exploration, namely modeling the problem with an optimal decision tree, reusing pixels from the previous step of the algorithm, and reducing the code footprint by means of Directed Rooted Acyclic Graphs. A complete and open-source benchmarking suite is also provided. Experimental results confirm that the proposed algorithms clearly outperform classical implementations

Complex Estimation of Strength Properties of Functional Materials on the Basis of the Analysis of Grain-Phase Structure Parameters

The technique allows analysis using grain-phase structure of the functional material to evaluate its performance, particularly strength properties. The technique is based on the use of linguistic variable in the process of comprehensive evaluation. An example of estimating the strength properties of steel reinforcement, subject to special heat treatment to obtain the desired grain-phase structure

Interpretation, Evaluation and the Semantic Gap ... What if we Were on a Side-Track?

International audienceA significant amount of research in Document Image Analysis, and Machine Perception in general, relies on the extraction and analysis of signal cues with the goal of interpreting them into higher level information. This paper gives an overview on how this interpretation process is usually considered, and how the research communities proceed in evaluating existing approaches and methods developed for realizing these processes. Evaluation being an essential part to measuring the quality of research and assessing the progress of the state-of-the art, our work aims at showing that classical evaluation methods are not necessarily well suited for interpretation problems, or, at least, that they introduce a strong bias, not necessarily visible at first sight, and that new ways of comparing methods and measuring performance are necessary. It also shows that the infamous {\em Semantic Gap} seems to be an inherent and unavoidable part of the general interpretation process, especially when considered within the framework of traditional evaluation. The use of Formal Concept Analysis is put forward to leverage these limitations into a new tool to the analysis and comparison of interpretation contexts

Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations

Plant pathogens cause significant losses to agricultural yields, and increasingly threaten food security, ecosystem integrity, and societies in general. Xylella fastidiosa (Xf) is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates a dramatically broadened geographic range. The Xf pathogen has thus re-emerged as a global threat, with its poorly contained expansion in Europe creating a socio-economic, cultural, and political disaster. Xf represents a threat of global proportion because it can infect over 350 plant species worldwide, and the early detection of Xf has been identified as a critical need for its eradication. Here, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography reveal Xf infection in trees before symptoms are visible. We obtained accuracies of disease detection exceeding 80% when high-resolution solar-induced fluorescence quantified by 3D simulations and thermal-based stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected via spectral plant trait alterations (presumed false positives) developed Xf symptoms four months later at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant trait alterations caused by Xf infection are detectable at the landscape scale before symptoms are visible, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.JRC.D.1-Bio-econom