scispace - formally typeset
Book ChapterDOI

Unlocking the Mechanism of Devanagari Letter Identification Using Eye Tracking

Reads0
Chats0
TLDR
Upon understanding the level of distortion acceptable for correct letter recognition and the processes involved in the identification of the letters, the OCR can be made more robust and the gap between human reading and machine reading can be narrowed down.
Abstract
The present day computers can outperform the human in many complicated tasks very precisely and efficiently. However, in many scenarios like pattern recognition and more importantly, character recognition; a school going child can outperform the sophisticated machines available today. The modern machines present today find handwritten, calligraphic text difficult to recognize because such texts hardly contain rationalized straight lines or perfect loops or circles. Therefore, most of the optical character recognition systems fail to recognize the characters beyond certain levels of distortions and noise. On the other hand, the human brain has achieved a remarkable ability to recognize visual patterns or characters in various distortion conditions with high speed. The present work tries to understand how human perceive, process and recognize the Devanagari characters under various distortion levels. In order to achieve this objective, eye tracking experiment was performed on 20 graduate participants by presenting stimuli in decreasing level of distortions (from highly distorted to more normal one). The eye fixation patterns along with the time course of recognition gave us the moment-to-moment processing involved in letter identification. Upon understanding the level of distortion acceptable for correct letter recognition and the processes involved in the identification of the letters, the OCR can be made more robust and the gap between human reading and machine reading can be narrowed down.

read more

Citations
More filters
Book ChapterDOI

Effect of Devanagari Font Type in Reading Comprehension: An Eye Tracking Study

TL;DR: There is a scope for improvement in the reading comprehension, by changing the physical properties of the document without changing its content, when the same document is read in different font type.
Journal ArticleDOI

Memorability-based image compression

TL;DR: The comparative analysis shows that the memorability-based compression outperforms the state-of-the-art compression techniques.
Proceedings ArticleDOI

Intelligent Identification of Ornamental Devanagari Characters Inspired by Visual Fixations

TL;DR: It is found that the convolutional neural network performs better when trained with the assistance of fixation information compared to the network trained without eye fixations.
Posted Content

Efficient Video Summarization Framework using EEG and Eye-tracking Signals.

TL;DR: In this paper, an efficient video summarization framework that will give a gist of the entire video in a few key-frames or video skims is proposed, which relies on the cognitive judgments of human beings.
Proceedings ArticleDOI

Collaborative Human Machine Attention Module for Character Recognition

TL;DR: Wang et al. as mentioned in this paper proposed a collaborative human and machine attention module which considers both visual and network's attention, which can be integrated with any convolutional neural network (CNN) model.
References
More filters
Journal ArticleDOI

Eye movements in reading and information processing: 20 years of research.

TL;DR: The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined.
Proceedings ArticleDOI

Identifying fixations and saccades in eye-tracking protocols

TL;DR: A taxonomy of fixation identification algorithms is proposed that classifies algorithms in terms of how they utilize spatial and temporal information in eye-tracking protocols in order to evaluate and compare these algorithms with respect to a number of qualitative characteristics.
Journal ArticleDOI

Indian script character recognition: a survey

TL;DR: A review of the OCR work done on Indian language scripts and the scope of future work and further steps needed for Indian script OCR development is presented.
Proceedings ArticleDOI

I know what you are reading: recognition of document types using mobile eye tracking

TL;DR: This work investigates whether different document types can be automatically detected from visual behaviour recorded using a mobile eye tracker, and presents an initial recognition approach that uses special purpose eye movement features as well as machine learning for document type detection.