{rfName}
Ha

Indexed in

License and use

Citations

13

Altmetrics

Grant support

Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.

Analysis of institutional authors

Castro-Bleda, MjCorresponding AuthorEspana-Boquera, SAuthor

Share

October 30, 2024
Publications
>
Article
No

Handwriting recognition by using deep learning to extract meaningful features

Publicated to:Ai Communications. 32 (2): 101-112 - 2019-01-01 32(2), DOI: 10.3233/AIC-170562

Authors: Pastor-Pellicer, Joan; Jose Castro-Bleda, Maria; Espana-Boquera, Salvador; Zamora-Martinez, Francisco

Affiliations

Univ Politecn Valencia, Camino Vera S-N - Author
Veridas SL, R&D Dept, Pol Ind Talluntxe 2 - Author

Abstract

Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature extraction in the area of pattern recognition, especially for Computer Vision or Speech tasks. In this work, we directly use raw text line images by feeding them to Convolutional Neural Networks and deep Multilayer Perceptrons for feature extraction in a Handwriting Recognition system. The proposed recognition system, based on Hidden Markov Models that are hybridized with Neural Networks, has been tested with the IAM Database, achieving a considerable improvement.

Keywords

Character recognitionConvolutionConvolutional neural networkConvolutional neural networksDeep learningExtractionFeature extractionFutureHandwriting recognitionHidden markov modelsLearning techniquesModelsMultilayer neural networksNetworkOnlineParametrizationsRaw signalsRecognition systemsSpeech recognitionText lines

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ai Communications, and although the journal is classified in the quartile Q4 (Agencia WoS (JCR)), its regional focus and specialization in Computer Science, Artificial Intelligence, give it significant recognition in a specific niche of scientific knowledge at an international level.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.94, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Aug 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-08-09, the following number of citations:

  • WoS: 5
  • Scopus: 8

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-09:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 18 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Pastor-Pellicer, J) .

the author responsible for correspondence tasks has been Castro Bleda, María José.