Word Retrieval in Historical Document Using Character-Primitives

Roy, Partha Pratim; Ramel, Jean-Yves; Ragot, Nicolas

doi:10.1109/icdar.2011.142

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…1. From a scanned original document, we use the Agora and Retro software * developed by Ramel et al [15][16][17] at the University of Tours, France. This step allows us to extract all instances of the characters used in the document, and group them together, in order to obtain sets of 'a', b' ...…”

Section: Process Descriptionmentioning

confidence: 99%

Re-typograph phase I: a proof-of-concept for typeface parameter extraction from historical documents

Bouville¹,

Blégean²,

Cao³

et al. 2015

SPIE Proceedings

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This paper reports on the first phase of an attempt to create a full retro-engineering pipeline that aims to construct a complete set of coherent typographic parameters defining the typefaces used in a printed homogenous text. It should be stressed that this process cannot reasonably be expected to be fully automatic and that it is designed to include human interaction. Although font design is governed by a set of quite robust and formal geometric rulesets, it still heavily relies on subjective human interpretation. Furthermore, different parameters, applied to the generic rulesets may actually result in quite similar and visually difficult to distinguish typefaces, making the retro-engineering an inverse problem that is ill conditioned once shape distortions (related to the printing and/or scanning process) come into play. This work is the first phase of a long iterative process, in which we will progressively study and assess the techniques from the state-of-the-art that are most suited to our problem and investigate new directions when they prove to not quite adequate. As a first step, this is more of a feasibility proof-of-concept, that will allow us to clearly pinpoint the items that will require more in-depth research over the next iterations.

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Section: Process Descriptionmentioning

confidence: 99%

Re-typograph phase I: a proof-of-concept for typeface parameter extraction from historical documents

Bouville¹,

Blégean²,

Cao³

et al. 2015

SPIE Proceedings

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“…Changes in the writing hand, skewness, and also changes in the baseline are some of the key problems. It has as well been observed that, in old manuscripts, the writers have not well distinguished between inter-word spaces and intra-word spaces (inter-letters, for example) [1]. This phenomenon seems to be universal for all the languages [2].…”

Section: Introductionmentioning

confidence: 99%

“…One direction is to use a primitive-or graphemebased representation in order to create a grammar for the huge set of possible character shapes based on a limited set of graphemes [1]. However, graphemes require a subcharacter segmentation, and this segmentation could be an ill-posed problem because the ground-truth data is at the character level, not at the grapheme level.…”

Section: Introductionmentioning

confidence: 99%

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A new framework based on signature patches, micro registration, and sparse representation for optical text recognition

Moghaddam

Cheriet

2012

2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA)

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A framework for development of segmentation-free optical recognizers of ancient manuscripts, which work free from line, word, and character segmentation, is proposed. The framework introduces a new representation of visual text using the concept of signature patches. These patches which are free from traditional guidelines of text, such as the baseline, are registered to each other using a microscale registration method based on the estimation of the active regions using a multilevel classifier, the directional map. Then, an one-dimensional feature vector is extracted from the registered signature patches, named spiral features. The incremental learning process is performed using a sparse representation using a dictionary of spiral feature atoms. The framework is applied to the George Washington database with promising results.

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“…For document images, the following databases are the most commonly used. For printed documents: Washington UW3 [2], LRDE [3], RETAS-OCR [4], PaRADIIT [5], etc. ; for handwritten documents IAM database [6], RIMES [7], GERMANA [8], etc.…”

Section: Introductionmentioning

confidence: 99%

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

et al. 2017

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Most digital libraries that provide user-friendly interfaces, enabling quick and intuitive access to their resources, are based on Document Image Analysis and Recognition (DIAR) methods. Such DIAR methods need ground-truthed document images to be evaluated/compared and, in some cases, trained. Especially with the advent of deep learning-based approaches, the required size of annotated document datasets seems to be ever-growing. Manually annotating real documents has many drawbacks, which often leads to small reliably annotated datasets. In order to circumvent those drawbacks and enable the generation of massive ground-truthed data with high variability, we present DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled ground truth. DocCreator has been used in various experiments, showing the interest of using such synthetic images to enrich the training stage of DIAR tools.

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Word Retrieval in Historical Document Using Character-Primitives

Cited by 16 publications

References 9 publications

Re-typograph phase I: a proof-of-concept for typeface parameter extraction from historical documents

Re-typograph phase I: a proof-of-concept for typeface parameter extraction from historical documents

A new framework based on signature patches, micro registration, and sparse representation for optical text recognition

DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images

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