Word matching using single closed contours for indexing handwritten historical documents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 Since traditional handwriting recognizers based on optical character recognition (OCR) do not perform well on historical documents, recently a holistic word recognition approach has gained in popularity as an attractive and more straightforward solution (Lavrenko et al.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

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