5 research outputs found
Inkball Models for Character Localization and Out-of-Vocabulary Word Spotting
Inkball models have previously been used for keyword spotting under the whole word query-by-image paradigm. This paper applies inkball methods to string-based queries for the first time, using synthetic models composed from individual characters. A hybrid system using both query-by-string for unknown words and query-by-example for known words outperforms either approach by itself on the George Washington and Parzival test sets. In addition, inkball character models offer an explanatory tool for understanding handwritten markings. In combination with a transcript they can help to to attribute each ink pixel of a word image to specific letters, resulting in highquality character segmentations
Symmetric Inkball Alignment with Loopy Models
Alignment tasks generally seek to establish a spatial correspondence between two versions of a text, for example between a set of manuscript images and their transcript. This paper examines a different form of alignment problem, namely pixel-scale alignment between two renditions of a handwritten word or phrase. Using loopy inkball graph models, the proposed technique finds spatial correspondences between two text images such that similar parts map to each other. The method has applications to word spotting and signature verification, and can provide analytical tools for the study of handwriting variation
Graph-Based Offline Signature Verification
Graphs provide a powerful representation formalism that offers great promise
to benefit tasks like handwritten signature verification. While most
state-of-the-art approaches to signature verification rely on fixed-size
representations, graphs are flexible in size and allow modeling local features
as well as the global structure of the handwriting. In this article, we present
two recent graph-based approaches to offline signature verification: keypoint
graphs with approximated graph edit distance and inkball models. We provide a
comprehensive description of the methods, propose improvements both in terms of
computational time and accuracy, and report experimental results for four
benchmark datasets. The proposed methods achieve top results for several
benchmarks, highlighting the potential of graph-based signature verification
Automatable Annotations – Image Processing and Machine Learning for Script in 3D and 2D with GigaMesh
Libraries, archives and museums hold vast numbers of objects with script in 3D such as inscriptions, coins, and seals, which provide valuable insights into the history of humanity. Cuneiform tablets in particular provide access to information on more than three millennia BC. Since these clay tablets require an extensive examination for transcription, we developed the modular GigaMesh software framework to provide high-contrast visualization of tablets captured with 3D acquisiton techniques. This framework was extended to provide digital drawings exported as XML-based Scalable Vector Graphics (SVG), which are the fundamental input of our approach inspired by machine-learning techniques based on the principle of word spotting. This results in a versatile symbol-spotting algorithm to retrieve graphical elements from drawings enabling automated annotations. Through data homogenization, we achieve compatibility to digitally born manual drawings, as well as to retro-digitized drawings. The latter are found in large Open Access databases, e.g. provided by the Cuneiform Database Library Initiative (CDLI). Ongoing and future work concerns the adaptation of filtering and graphical query techniques for two-dimensional raster images widely used within Digital Humanities research
Analyzing Handwritten and Transcribed Symbols in Disparate Corpora
Cuneiform tablets appertain to the oldest textual artifacts used for more than
three millennia and are comparable in amount and relevance
to texts written in Latin or ancient Greek.
These tablets are typically found in the Middle East and were
written by imprinting wedge-shaped impressions into wet clay.
Motivated by the increased demand for computerized analysis of documents within
the Digital Humanities, we develop the foundation for quantitative processing
of cuneiform script.
Using a 3D-Scanner to acquire a cuneiform tablet or manually creating line
tracings are two completely different representations of the same type of text
source. Each representation is typically processed with its own tool-set and
the textual analysis is therefore limited to a certain type of digital
representation. To homogenize these data source a unifying minimal wedge
feature description is introduced. It is extracted by
pattern matching and subsequent conflict resolution
as cuneiform is written densely with highly overlapping wedges.
Similarity metrics for cuneiform signs based on distinct
assumptions are presented. (i) An implicit model represents cuneiform signs
using undirected mathematical graphs and measures the similarity of
signs with graph kernels.
(ii) An explicit model approaches the problem of recognition by an optimal
assignment between the wedge configurations of two signs.
Further, methods for spotting cuneiform script are developed, combining
the feature descriptors for cuneiform wedges with prior work on
segmentation-free word spotting using part-structured models.
The ink-ball model is adapted by treating wedge feature descriptors as
individual parts.
The similarity metrics and the adapted spotting model are both evaluated
on a real-world dataset outperforming the state-of-the-art in
cuneiform sign similarity and spotting.
To prove the applicability of these methods for computational cuneiform
analysis, a novel approach is presented for mining frequent
constellations of wedges resulting in spatial n-grams. Furthermore,
a method for automatized transliteration of tablets is evaluated by
employing structured and sequential learning on a dataset of
parallel sentences. Finally, the conclusion
outlines how the presented methods enable the development of new tools
and computational analyses, which are objective and reproducible,
for quantitative processing of cuneiform script