Word matching using single closed contours for indexing handwritten historical documents

Effective indexing is crucial for providing convenient access to scanned versions of large collections of historically valuable handwritten manuscripts. Since traditional handwriting recognizers based on optical character recognition (OCR) do not perform well on historical documents, recently a holistic word recognition approach has gained in popularity as an attractive and more straightforward solution (Lavrenko et al. in proc. document Image Analysis for Libraries (DIAL’04), pp. 278–287, 2004). Such techniques attempt to recognize words based on scalar and profile-based features extracted from whole word images. In this paper, we propose a new approach to holistic word recognition for historical handwritten manuscripts based on matching word contours instead of whole images or word profiles. The new method consists of robust extraction of closed word contours and the application of an elastic contour matching technique proposed originally for general shapes (Adamek and O’Connor in IEEE Trans Circuits Syst Video Technol 5:2004). We demonstrate that multiscale contour-based descriptors can effectively capture intrinsic word features avoiding any segmentation of words into smaller subunits. Our experiments show a recognition accuracy of 83%, which considerably exceeds the performance of other systems reported in the literature

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

Coupling Soybean Cyst Nematode Damage to CROPGRO-Soybean

The soybean cyst nematode (SCN) Heterodera glycines Ichinohe is responsible for substantial economic losses in soybean (Glycine max L. Merr.) production throughout the U.S. Results from past efforts to quantify the severity of crop damage resulting from SCN are often subject to variable experimental conditions resulting from differences in weather, soil type, and cultivar. Because of the difficulty in accounting for these variables, a process–oriented crop growth simulation model was chosen as a platform for studying the dynamics of SCN damage and for transferring knowledge between crop production scenarios. The objective of this study was to develop and evaluate hypotheses for coupling SCN damage to the process–oriented crop growth model CROPGRO–Soybean. A monomolecular function was used to relate daily SCN damage to initial population density of SCN eggs. The equation was incorporated into the crop model in order to test two hypotheses of how SCN damage occurs. The first hypothesis was that SCN reduce daily photosynthesis (Pg), while the second hypothesis was that SCN reduce daily potential root water uptake (RWU). Canopy biomass data collected in 1997 and 1998 from a site in Iowa were used to estimate damage function parameters for two distinct coupling points, one applied to reduce daily photosynthesis (Pg) and the other applied to reduce daily potential root water uptake (RWU). Function parameters were estimated by minimizing the log transformation of root mean square error (RMSE) between predicted and measured canopy biomass collected every 2 weeks during the season in Iowa. Biomass data collected in 1997 and 1998 from an independent site in Missouri were used to validate the SCN damage models. The minimum root mean squared errors (RMSE) of canopy and grain biomass were 0.245 and 0.198 log10(kg ha–1), respectively, for the RWU coupling point, and 0.238 and 0.193 log10(kg ha–1), respectively, for the Pg coupling point at the independent site in Missouri. The damage functions transferred very well to the independent site. Validation showed that the Pg coupling point represented the variability of both canopy and final yield data slightly better than the RWU coupling point

Soybean Cyst Nematode Reduces Soybean Yield Without Causing Obvious Aboveground Symptoms

Field experiments were conducted at locations in northern and southern Illinois, central Iowa, and central Missouri from 1997 to 1999 to investigate the effects of Heterodera glycines on soybean growth, development, and yield. A wide range of infestation levels was present at all locations. Two locally adapted cultivars, one resistant to H. glycines, were grown at each location. Cultivars were planted in alternating four-row strips with 76 cm between rows. For each cultivar, 20 1-m-long single-row plots were sampled every 2 weeks starting 4 weeks after planting. Infection by H. glycines reduced plant height and leaf and stem weight on the resistant cultivars in the first 12 weeks after planting, and delayed pod and seed development 12 to 14 weeks after planting. Biomass accumulation was not reduced on the susceptible cultivars until 10 weeks after planting; reduction in pod and seed development occurred throughout the reproductive stages. Susceptible cultivars produced significantly lower yields than resistant cultivars, but the yield reductions were not accompanied by visually detectable symptoms

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

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice