| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 567 | 34 | 36 | 10 |
International Journal on Document Analysis and Recognition mainly tackles studies in Pattern recognition (psychology), Artificial intelligence, Pattern recognition, Speech recognition and Natural language processing. In addition to Pattern recognition (psychology) research, the journal aims to explore topics under Word (computer architecture), Set (abstract data type), Handwriting recognition, Image (mathematics) and Information retrieval. The studies on Information retrieval discussed can also contribute to research in the domains of Image retrieval, Data mining and Document Structure Description.
Many of the studies tackled connect Artificial intelligence with a similar field of study like Computer vision. Image processing is a major topic of Computer vision research presented in it. The study on Pattern recognition presented is investigated in conjunction with research in Pixel.
Intelligent character recognition, Intelligent word recognition and Word recognition are some topics wherein Speech recognition research discussed in International Journal on Document Analysis and Recognition have an impact. Topics in Natural language processing explored in it were investigated in conjunction with research in Character (computing) and Identification (information).
The journal papers are organized to reinforce research efforts on Pattern recognition (psychology), Artificial intelligence, Speech recognition, Natural language processing and Optical character recognition. The journal publications address concerns in Pattern recognition (psychology) which are intertwined with other disciplines, such as Handwriting, Handwriting recognition, Feature extraction, Information retrieval and Character (computing). While Artificial intelligence is the focus of the journal papers, it also provides insights into the studies of Word recognition, Computer vision and Pattern recognition.
International Journal on Document Analysis and Recognition focuses on Pattern recognition (psychology), Artificial intelligence, Natural language processing, Deep learning and Information retrieval. The studies in Pattern recognition (psychology) featured incorporate elements of Image (mathematics), Optical character recognition, Artificial neural network, Set (abstract data type) and Character (computing). The journal facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Machine learning, Code (cryptography) and Pattern recognition.
The journal explores issues in Pattern recognition which can be linked to other research areas like Sequence and Digital image. The concepts on Natural language processing presented in the journal can also apply to other research fields, including Segmentation, Classifier (linguistics), Metric (mathematics), Field (computer science) and Pattern detection. The work on Information retrieval tackled in International Journal on Document Analysis and Recognition brings together disciplines like Text mining, Cognitive neuroscience of visual object recognition, Semantic interpretation and Rendering (computer graphics).
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in International Journal on Document Analysis and Recognition (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in International Journal on Document Analysis and Recognition (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 7.41% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 28.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.00% of all publications and 56.00% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
The International Journal on Document Analysis and Recognition provides an invaluable resource for academic professionals and aspiring students looking to contribute to the fields of Pattern recognition, Artificial intelligence, Natural language processing, and many more. These fields, in particular, provide numerous career paths in academia as well as practical industry applications. For potential authors and contributors, knowing the journeys and pathways through an academic career in these areas can be beneficial and insightful. To that end, understanding the steps to becoming a college or university professor or researcher would provide a valuable resource for readers. For instance, in the field of Artificial Intelligence (AI), a standard progression in an academic career might start with gaining a degree in Computer Science or a related field. This industry is particularly diverse and active, with research opportunities in numerous sub-fields such as Machine Learning, Neural Networks, Robotics, and Natural Language Processing. In the field of education, a similar progression is observed. For example, in Louisiana, there are particular steps and requirements necessary for someone aspiring to become a teacher. An aspiring teacher could learn about these steps through reliable resources detailing how to become a teacher in Louisiana. Just as you would follow a path to become a teacher in Louisiana, an academic professional could pursue a career in Natural Language Processing, Pattern Recognition, or any other field discussed and referenced in the International Journal on Document Analysis and Recognition. Ultimately, understanding these career paths and trajectories not only guides potential researchers and industry professionals but also enhances the very studies and topics tackled in the journal itself.
Victor Carbune;Pedro Gonnet;Thomas Deselaers;Henry A. Rowley
(2020)Chee-Kheng Ch’ng;Chee Seng Chan;Cheng-Lin Liu
(2020)Sanket Biswas;Pau Riba;Josep Lladós;Umapada Pal
(2021)Bernhard Schäfer;Margret Keuper;Heiner Stuckenschmidt
(2021)Yahia Hamdi;Houcine Boubaker;Adel M. Alimi;Adel M. Alimi
(2021)Rubén Tolosana;Rubén Vera-Rodríguez;Richard M. Guest;Julian Fiérrez
(2020)For those considering a Computer Science degree in the USA, exploring related fields can expand career opportunities and skill sets. Online degrees such as mechanical engineering provide practical and technical know-how, and the cheapest mechanical engineering degree online options make it accessible for many students.
Physics, especially theoretical physics, complements computer science by deepening analytical and problem-solving skills. Pursuing an online theoretical physics degree is a great way to broaden scientific understanding and enhance computational methods knowledge.
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Additionally, electrical engineering has substantial overlap with computer science, particularly in hardware and embedded systems. Students interested in this area can pursue an online master’s in electrical engineering degree, which offers flexible learning with a focus on innovative technology.
