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  • 1.
    Alonso, Pedro
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Saini, Rajkumar
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Kovács, György
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    TheNorth at HASOC 2019: Hate Speech Detection in Social Media Data2019Konferensbidrag (Refereegranskat)
    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.

  • 2.
    Saini, Rajkumar
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Dobson, Derek
    FamilySearch, USA.
    Morrey, Jon
    FamilySearch, USA.
    Liwicki, Marcus
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Foteini
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records2019Ingår i: ICDAR 2019: ICDAR 2019 HDRC Chinese, 2019Konferensbidrag (Refereegranskat)
    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this paper, we present a large historical database of Chinese family records with the aim to develop robust systems for historical document analysis. In this direction, we propose a Historical Document Reading Challenge on Large Chinese Structured Family Records (ICDAR 2019 HDRCCHINESE).The objective of the competition is to recognizeand analyze the layout,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).

  • 3.
    Saini, Rajkumar
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Kumar, Pradeep
    IIT Roorkee, India.
    Patidar, Shweta
    IIT Roorkee, India.
    Roy, Partha
    IIT Roorkee, India.
    Liwicki, Marcus
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Trilingual 3D Script Identification and Recognition using Leap Motion Sensor2019Ingår i: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, 2019, Vol. 5, s. 24-28Konferensbidrag (Övrigt vetenskapligt)
    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.

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