A robust realtime reading-skimming classifier

Biedert, Ralf; Hees, J.J. van; Dengel, Andreas; Buscher, Georg

doi:10.1145/2168556.2168575

Cited by 67 publications

(34 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to analyze reading behavior, several classification techniques have been studied. Biedert et al [2] proposed a robust approach that distinguishes whether the fixation is reading or skimming using gaze characteristics. They considered the forward speed and angularity as features for machine learning classification.…”

Section: Related Workmentioning

confidence: 99%

“…2, which we defined empirically based on the observed gaze patterns, and then constructed a classifier that labels the transitions. The features we employed for modeling the classifier are based on previous studies [2], [7]. The features are mainly gaze-, segment-, and transition-oriented geometrical characteristics, and not only extracted from a transition that is focused on, but also from two transitions before and after the current one.…”

Section: Classification Of Segment Transitionsmentioning

confidence: 99%

“…According to Refs. [2] and [7], saccade angularity and its direction are useful features for forecasting reading behavior. Our results confirmed similar trends.…”

Section: Experiments 1: Classification Performancementioning

confidence: 99%

See 2 more Smart Citations

Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Yamaya

Topić

Aizawa

2017

Journal of Information Processing

View full text Add to dashboard Cite

Abstract:In eye-tracking-based reading behavior research, gaze sampling errors often negatively affect gaze-to-word mapping. In this paper, we propose a method for more accurate mapping by first taking adjacent horizontally progressive fixations as segments, and then classifying the segments into six classes using a random forest classifier. The segments are then reconstructed based on the classification, and are associated with a document line using a dynamic programming algorithm. The combination of segment-to-line mapping and transition classification achieved 87% mapping accuracy. We also witnessed a reduction of manual annotation time when the mapping was used as an annotation guiding tool.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Classification Of Segment Transitionsmentioning

confidence: 99%

See 1 more Smart Citation

Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Yamaya

Topić

Aizawa

2017

Journal of Information Processing

View full text Add to dashboard Cite

show abstract

“…This analysis is hard to perform because of the inaccuracy of eye trackers [3]. The inaccuracy can be caused by miscalibration, head movements, lighting change, etc.…”

Section: Introductionmentioning

confidence: 99%

Vertical error correction of eye trackers in nonrestrictive reading condition

Sanches

Augereau

Kise

2016

IPSJ T Comput Vis Appl

View full text Add to dashboard Cite

The eye tracking technology is used for four decades for studying reading behavior. The applications are various: estimating the reader comprehension, identifying the reader, summarizing a read document, creating a reading-life log, etc. The gaze data used in such applications has to be accurate enough to perform the analysis. In order to improve the accuracy, most of the experiments are set up with restrictive conditions such as using a head fixation and a professional eye tracker. It implies that the results are valid only in restrictive laboratory settings and an unrealistic small error is produced by the experiment. However, the use of affordable eye trackers in realistic conditions of reading leads to large errors in the recordings. We propose a new algorithm to correct the vertical error and to align the gazes with the text. The proposed algorithm is robust to rereading and skipping some parts of text, contrary to all the other algorithms of the state of the art. We show that up to 69 % of the gazes are aligned with the correct text lines.

show abstract

“…Buscher et al [12] revealed the relations between eye movement measures and user-perceived relevance for personal information retrieval application. To distinguish the reading or skimming behavior in the eye tracking data, Biedert et al [13] extracted average forward speed and angularity features to train a classifier. A fresh work submitted by Kai et al [14] attempted to measure brain electrical activity by an off-the-shelf EEG device for distinguishing genres of documents.…”

Section: Introductionmentioning

confidence: 99%

The Reading-Life Log -- Technologies to Recognize Texts That We Read

Kimura

Uchida

Omachi

et al. 2013

2013 12th International Conference on Document Analysis and Recognition

View full text Add to dashboard Cite

Abstract-Reading life log is a type of techniques to automatically and unconsciously record people's reading intentions, interests and habits. Besides, it can also serve as various assistants in our daily life. In this paper, a reading-life log system is implemented by a head-mounted and unobtrusive video camera with a high resolution and a high shutter speed. We utilize DP matching, and propose a text-based frame mosaicing method to integrate multiple frames in a clip. The developed system is tested in the various environments indoor and outdoor. The experimental results show that our system can provide reliable outputs with respect to the most correct responses. The infrequent misregistration between lines also indicates the feasibility and validity of the text-based frame mosaicing.

show abstract

A robust realtime reading-skimming classifier

Cited by 67 publications

References 11 publications

Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Vertical error correction of eye trackers in nonrestrictive reading condition

The Reading-Life Log -- Technologies to Recognize Texts That We Read

Contact Info

Product

Resources

About