Deep convolutional neural networks are today the new baseline for a wide range of machine vision tasks. The problem of keyword spotting is no exception to this rule. Many successful network architectures and learning strategies have been adapted from other vision tasks to create successful keyword spotting systems. In this paper, we argue that various details concerning this adaptation could be re-examined, to the end of building stronger spotting models. In particular, we examine the usefulness of a pyramidal spatial pooling layer versus a simpler approach, and show that a zoning strategy combined with fixed-size inputs can be just as effective while less computationally expensive. We also examine the usefulness of augmentation, class balancing and ensemble learning strategies and propose an improved network. Our hypotheses are tested with numerical experiments on the IAM document collection, where the proposed network outperforms all other existing models.

Nonlinear manifold embedding has attracted considerable attention due to its highly-desired property of efficiently encoding local structure, i.e. intrinsic space properties, into a low-dimensional space. The benefit of such an approach is twofold: it leads to compact representations while addressing the often-encountered curse of dimensionality. The latter plays an important role in retrieval applications, such as keyword spotting, where a sorted list of retrieved objects with respect to a distance metric is required. In this work, we explore the efficiency of the popular manifold embedding method t-distributed Stochastic Neighbor Embedding (t-SNE) on the Query-by-Example keyword spotting task. The main contribution of this work is the extension of t-SNE in order to support out-of-sample (OOS) embedding which is essential for mapping query images to the embedding space. The experimental results demonstrate a significant increase in keyword spotting performance when the word similarity is calculated on the embedding space.

In this paper, we propose a novel probabilistic model for lexicon-free handwriting recognition. Model inputs are word images encoded as Pyramidal Histogram Of Character (PHOC) vectors. PHOC vectors have been used as efficient attribute-based, multi-resolution representations of either text strings or word image contents. The proposed model formulates PHOC decoding as the problem of finding the most probable sequence of characters corresponding to the given PHOC. We model PHOC layers as Beta-distributed observations, linked to hidden states that correspond to character estimates. Characters are in turn linked to one another along a Markov chain, encoding language model information. The sequence of characters is estimated using the max-sum algorithm in a process that is akin to Viterbi decoding. Numerical experiments on the well-known George Washington database show competitive recognition results.

The result of a document image segmentation task, e.g. text line or word segmentation, is usually a labeled image with each label corresponding to a different segmented region. For many applications, the segmented regions need to be stored and represented in an efficient way, using simple geometric shapes. A challenging task is to restrict all pixels corresponding to a specific label inside a polygon with a minimum number of vertices. Such a polygon promotes the description simplicity and the storage efficiency, while providing a much more user-friendly representation that can be edited easily. The proposed method is a cost-effective approximation of the minimum-edges polygon problem, computing a contour enclosing only pixels of a certain label and using a greedy algorithm in order to reduce the contour into a minimum-link polygon that retains the separability property between the labeled set of pixels.

In this paper, we present a novel approach for segmentation-based handwritten keyword spotting. The proposed approach relies upon the extraction of a simple yet efficient descriptor which is based on projections of oriented gradients. To this end, a global and a local word image descriptors, together with their combination, are proposed. Retrieval is performed using to the euclidean distance between the descriptors of a query image and the segmented word images. The proposed methods have been evaluated on the dataset of the ICFHR 2014 Competition on handwritten keyword spotting. Experimental results prove the efficiency of the proposed methods compared to several state-of-the-art techniques.

Recognition of old Greek document images containing polytonic (multi accent) characters is a challenging task due to the large number of existing character classes (more than 270) which cannot be handled sufficiently by current OCR technologies. Taking into account that the Greek polytonic system was used from the late antiquity until recently, a large amount of scanned Greek documents still remains without full test search capabilities. In order to assist the progress of relevant research, this paper introduces the first publicly available old Greek polytonic database GRPOLY-DB for the evaluation of several document image processing tasks. It contains both machine-printed and handwritten documents as well as annotation with ground-truth information that can be used for training and evaluation of the most commou document image processing tasks, i.e.. text line and word segmentation, test recognition, isolated character recognition and word spotting. Results using several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of old Greek document image recognition and word spotting.

In this paper, we present a new approach for off-line isolated character recognition. The proposed method relies upon the application of a projection-based feature extraction stage, which resembles the Radon transform, on both the original image and a set of generated images corresponding to different gradient orientations of the original image. For the classification stage, Support Vectors Machines (SVM) are used. The proposed method is evaluated using one typewritten (GRPOLY-DB - Historical Greek) and two handwritten (CIL - Greek, CEDAR - English) publicly available databases. Experimental results prove the efficiency of the proposed method compared to several state-of-the-art techniques.