Oriol Ramos Terrades

The GERMANA Database

A new handwritten text database, GERMANA, is presented to facilitate empirical comparison of different approaches to text line extraction and off-line handwriting recognition. GERMANA is the result of digitising and annotating a 764-page Spanish manuscript from 1891, in which most pages only contain nearly calligraphed text written on ruled sheets of well-separated lines. To our knowledge, it is the first publicly available database for handwriting research, mostly written in Spanish and comparable in size to standard databases. Due to its sequential book structure, it is also well-suited for realistic assessment of interactive handwriting recognition systems. To provide baseline results for reference in future studies, empirical results are also reported, using standard techniques and tools for preprocessing, feature extraction, HMM-based image modelling, and language modelling.

Histogram of radon transform. A useful descriptor for shape retrieval

In this paper we present a new descriptor based on the Radon transform. We propose a histogram of the Radon transform, called HRT, which is invariant to common geometrical transformations. For black and white shapes, the HRT descriptor is a histogram of shape lengths at each orientation. The experimental results, defined on different databases and compared with several well-known descriptors, show the robustness of our method.

Radon transform for linear symbol representation

Content-based retrieval and recognition of graphic images requires good models for symbol representation, able to identify those features providing the most relevant information about the shape and the visual appearance of symbols. In this work we have used the Radon transform as the basis to extract the representation of graphic images as it permits to globally detect lineal singularities in an image, which are the most important source of information in these images. The image obtained after applying Radon transform can be used directly to describe the symbol, or can be used to extract new and compact descriptors from it, which will also be based on lineal information about the image. We present some preliminary results showing the usefulness of this representation with a set of architectural symbols.

Feature selection combining genetic algorithm and Adaboost classifiers

This paper presents a fast method using simple genetic algorithms (GAs) for features selection. Unlike traditional approaches using GAs, we have used the combination of Adaboost classifiers to evaluate an individual of the population. So, the fitness function we have used is defined by the error rate of this combination. This approach has been implemented and tested on the MNIST database and the results confirm the effectiveness and the robustness of the proposed approach.

A Review of Shape Descriptors for Document Analysis

Shape descriptors play an important role in many document analysis application. In this paper we review some of the shape descriptors proposed in the last years from a new point of view. We propose the definitions of descriptor and primitive and introduce the notion of feature extraction method. With these definitions, we propose a new classification of shape descriptors that permits to classify according to their properties pointing out their strengths and weaknesses.

SVM Based Scheme for Thai and English Script Identification

In some Thai documents, a single text line of a document page may contain both Thai and English scripts. For the optical character recognition (OCR) of such a document page it is better to identify, at first, Thai and English script portions and then to use individual OCR system of the respective scripts on these identified portions. In this paper, a SVM based method is proposed for identification of word-wise printed English and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of the individual character group combining different character features obtained from structural shape, profile, component overlapping information, topological properties, water reservoir concept etc. Based on the experiment on 6110 data we obtained 99.36% script identification accuracy from the proposed scheme.

Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval

In this paper we present an adaptive method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a largeset of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising.

Confidence Measures for Error Correction in Interactive Transcription Handwritten Text

An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence.

Flowchart recognition for non-textual information retrieval in patent search

Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset.

Sparse representation over learned dictionary for symbol recognition

In this paper we propose an original sparse vector model for symbol retrieval task. More specifically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. HighlightsWe study how to use sparse representations for symbol description in retrieval tasks.We propose an original extension of the tf-idf model to sparse representations.We use the K-SVD algorithm using the shape context descriptor applied on keypoints.This is the first attempt of using this kind of representation in symbol retrieval tasks.