This paper presents a textline detection method for degraded historical documents. Our method follows a conventional two-step procedure that the binarization is first performed and then the textlines are extracted from the binary image. In order to address the challenges in historical documents such as document degradation, structure noise, and skews, we develop new methods for the binarization...

0167-8655/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.patrec.2011.02.013 ⇑ Corresponding author. Tel.: +90 312 2903143; fax E-mail addresses: [email protected] (E.F. Ca (P. Duygulu). In this study, we propose a newmethod for retrieving and recognizing words in historical documents. We represent word images with a set of line segments. Then we provide a criterion for word matchin...

Spatial selections are a ubiquitous concept in visualization. By localizing particular features, they can be analysed and compared in different views. However, the semantics of such selections often depend on specific parameter settings and it can be difficult to reconstruct them without additional information. In this paper, we present the concept of contextual snapshots as an effective means ...

Structural credit risk models have faced difficulties in matching observed market credit spreads while simultaneously matching default rates, recoveries, leverage and risk premia a shortcoming that has become known as the credit spread puzzle. We ask whether stochastic asset volatility, as an extension to this model class, has the ability to help resolve this puzzle. We identify that although t...

In this paper, a classification-free Word-Spotting system, appropriate for the retrieval of printed historical document images is proposed. The system skips many of the procedures of a common approach. It does not include segmentation, feature extraction or classification. Instead it treats the queries as compact shapes and uses image processing techniques in order to localize a query in the do...

This paper describes the restructuring process of a large corpus of historical documents and the system architecture that is used for accessing it. The initial challenge of this process was to get the most out of existing material, normalizing the legacy markup and harvesting the inherent information using widely available standards. This resulted in a conceptual and technical restructuring of ...

In this paper, we describe an input sensitive thresholding algorithm for ancient Hebrew calligraphy documents. Usually, historical document images are of poor quality since the documents have degraded over time due to storage conditions. However, the distribution of noise in one document is not uniform and the characters quality may vary. We develop tools to identify noisy characters and apply ...

Historical documents frequently exhibit extensive orthographic variation, including archaic spellings and obsolete shorthand. OCR tools typically seek to produce so-called diplomatic transcriptions that preserve these variants, but many end tasks require transcriptions with normalized orthography. In this paper, we present a novel joint transcription model that learns, unsupervised, a probabili...

Even though NLP tools are widely used for contemporary text today, there is a lack of tools that can handle historical documents. Such tools could greatly facilitate the work of researchers dealing with large volumes of historical texts. In this paper we propose a method for extracting verbs and their complements from historical Swedish text, using NLP tools and dictionaries developed for conte...

This paper describes the Gamera framework for building custom document recognition systems. This open-source system is designed to support the testand-refine development cycle: an important style for developing recognition systems that work with difficult historical documents, since the solutions are often non-obvious. This paper explains the overall architecture of the system, in addition to d...