Change search
Link to record
Permanent link

Direct link
BETA
Saini, Rajkumar, Dr.ORCID iD iconorcid.org/0000-0001-8532-0895
Publications (3 of 3) Show all publications
Saini, R., Dobson, D., Morrey, J., Liwicki, M. & Liwicki, F. (2019). ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records. In: ICDAR 2019: ICDAR 2019 HDRC Chinese. Paper presented at ICDAR 2019.
Open this publication in new window or tab >>ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records
Show others...
2019 (English)In: ICDAR 2019: ICDAR 2019 HDRC Chinese, 2019Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a large historical database of Chinese family records with the aim to develop robust systems for historical document analysis. In this direction, we propose a Historical Document Reading Challenge on Large Chinese Structured Family Records (ICDAR 2019 HDRCCHINESE).The objective of the competition is to recognizeand analyze the layout, and finally detect and recognize thetextlines and characters of the large historical document image dataset containing more than 10000 pages. Cascade R-CNN, CRNN, and U-Net based architectures were trained to evaluatethe performances in these tasks. Error rate of 0.01 has been recorded for textline recognition (Task1) whereas a Jaccard Index of 99.54% has been recorded for layout analysis (Task2).The graph edit distance based total error ratio of 1.5% has been recorded for complete integrated textline detection andrecognition (Task3).

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Identifiers
urn:nbn:se:ltu:diva-77258 (URN)
Conference
ICDAR 2019
Available from: 2019-12-27 Created: 2019-12-27 Last updated: 2020-01-23
Alonso, P., Saini, R. & Kovács, G. (2019). TheNorth at HASOC 2019: Hate Speech Detection in Social Media Data. In: : . Paper presented at Forum for Information Retrieval Evaluation 2019.
Open this publication in new window or tab >>TheNorth at HASOC 2019: Hate Speech Detection in Social Media Data
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The detection of hate speech in social media is a crucial task.The uncontrolled spread of hate speech can be detrimental to maintaining the peace and harmony in society. Particularly when hate speech isspread with the intention to defame people, or spoil the image of a person, a community, or a nation. A major ground for spreading hate speechis that of social media. This significantly contributes to the difficultyof the task, as social media posts not only include paralinguistic tools(e.g. emoticons, and hashtags), their linguistic content contains plentyof poorly written text that does not adhere to grammar rules. With therecent development in Natural Language Processing (NLP), particularly with deep architecture, it is now possible to anlayze unstructured composite natural language text. For this reason, we propose a deep NLPmodel for the detection of automatic hate speech in social media data. We have applied our model on the HASOC2019 hate speech corpus, and attained a macro F1 score of 0.63 in the detection of hate speech.

National Category
Engineering and Technology
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-77403 (URN)
Conference
Forum for Information Retrieval Evaluation 2019
Available from: 2020-01-14 Created: 2020-01-14 Last updated: 2020-01-23
Saini, R., Kumar, P., Patidar, S., Roy, P. & Liwicki, M. (2019). Trilingual 3D Script Identification and Recognition using Leap Motion Sensor. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW): . Paper presented at 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 20-25 september, 2019, Sydney, Australia (pp. 24-28). IEEE, 5
Open this publication in new window or tab >>Trilingual 3D Script Identification and Recognition using Leap Motion Sensor
Show others...
2019 (English)In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, 2019, Vol. 5, p. 24-28Conference paper, Published paper (Other academic)
Abstract [en]

Recently, the development of depth sensing technologies such as Leap motion and Microsoft Kinect sensors facilitate a touch-less environment to interact with computers and mobile devices. Several research have been carried out for the air-written text recognition with the help of these devices. However, there are several countries (like India) where multiple scripts are used to write official languages. Therefore, for the development of an effective text recognition system, the script of the text has to be identified first. The task becomes more challenging when it comes to 3D handwriting. Since, the 3D text written in air is consists of single stoke only. This paper presents a 3D script identification and recognition system written in three languages, namely, Hindi, English and Punjabi using Leap motion sensor. In the first stage, script identification was carried out in one of the three language. Next, Hidden Markov Model (HMM) was used to recognize the words. An accuracy of 96.4% was recorded in script identification whereas accuracies of 72.99%, 73.25% and 60.5% were recorded in script identification of Hindi, English and Punjabi scripts, respectively.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Document Analysis and Recognition Workshops (ICDARW)
Keywords
Air-writing, Leap motion, Word recognition, Script Identification, HMM
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-77257 (URN)10.1109/ICDARW.2019.40076 (DOI)000518786800005 ()978-1-7281-5054-3 (ISBN)
Conference
2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 20-25 september, 2019, Sydney, Australia
Available from: 2019-12-27 Created: 2019-12-27 Last updated: 2020-04-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8532-0895

Search in DiVA

Show all publications