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.

This tutorial focuses on recent advances and ongoing developments for historical document processing. It includes the main challenges involved, the different tasks that have to be implemented as well as practices and technologies that currently exist in the literature. The focus is given on the most promising techniques, related projects as well as on existing datasets and competitions that can be proved useful to historical document processing research.

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.

Word segmentation refers to the process of defining the word regions of a text line. It is a critical stage towards word and character recognition as well as word spotting and mainly concerns three basic stages, namely preprocessing, distance computation and gap classification. In this paper, we propose a novel word segmentation method which uses the Student's-t distribution for the gap classification stage. The main advantage of the Student's-t distribution concerns its robustness to the existence of outliers. In order to test the efficiency of the proposed method we used the four benchmarking datasets of the ICDAR/ICFHR Handwriting Segmentation Contests as well as a historical typewritten dataset of Greek polytonic text. It is observed that the use of mixtures of Student's-t distributions for word segmentation outperforms other gap classification methods in terms of Recognition Accuracy and F-Measure. Also, in terms of all examined benchmarks, the Student's-t is shown to produce a perfect segmentation result in significantly more cases than the state-of-the-art Gaussian mixture model.

In this paper we propose a new method for automated segmentation of scanned newspaper pages into articles. Article regions are produced as a result of merging sub-article level content and title regions. We use a Bayesian Gaussian mixture model to model page Connected Component information and cluster input into sub-article components. The Bayesian model is conditioned on a prior distribution over region features, aiding classification into titles and content. Using a Dirichlet prior we are able to automatically estimate correctly the number of title and article regions. The method is tested on a dataset of digitized historical newspapers, where visual experimental results are very promising.

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 address the problem of word spotting using a shape-based matching scheme between segmented word images represented by local contour features. As in a typical query-by-example (QBE) paradigm, a user selects an instance of the query word from the collection of interest and a ranked list of images is returned, based on their similarity with the query. This is accomplished in two steps. The query image is firstly aligned with the test image according to a similarity measure defined on their descriptors and then the aligned images are matched through a deformable non-rigid point matching algorithm. Experiments are carried out on historical handwritten text, written in Greek and English, respectively. Moreover, comparisons with other QBE methods show the efficiency of our system as well as its flexibility in adapting to different scripts.

Word spotting and recognition are among the most important applications used today in the field of document processing and text understanding. In word spotting, the goal is to search a scanned document for instances of a specific word. In word recognition, we aim to identify the transcription of the document words. While substantial work in both topics has been published, not all are readily adaptible to scripts other than a specific script and/or language. This is especially true for documents written in the polytonic greek script, a script used to write the greek language during a period that approximately spans two millenia. In this work, we extend the attribute-based model for word spotting and recognition recently presented in [1] for use with polytonic greek documents. To this end, we present three alternative ways to extend the model mechanism to handle the greek alphabet and its various combinations of diacritic marks. We have run numerical experiments over polytonic machine-printed and handwritten documents for word spotting and recognition. The proposed model is shown to outperform other state-of-the-art methods in word spotting trials. Regarding polytonic greek unconstrained handwritten word recognition, to the best of our knowledge, this is the first work to address the problem succesfully.