The right visual field advantage and the optimal viewing position effect: On the relation between foveal and parafoveal word recognition

Recent developments on the optimal viewing position (OVP) effect suggest that it may be caused by the same factors that underlie the right visual field advantage in word recognition. This raises the question of the relationship between foveal and parafoveal word recognition. Three experiments are reported in which participants identified tachistoscopically presented words that were presented randomly in foveal and parafoveal vision. The results show that both the OVP effect and the right visual field advantage for word recognition are part of a larger extended OVP curve that has the shape of a Gaussian distribution with the mode shifted to the left of the center of the stimulus word. The shift of the distribution is a function of word length, but not of presentation duration; it is also slightly moderated by the information value of word beginning and word end

Turkish handwritten text recognition: a case of agglutinative languages

We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40% for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56% top-10 word recognition rate

WordFences: Text localization and recognition

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a unique pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing by combining semantic word segmentation with a voting scheme for merging segmentations of multiple scales, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, end-to-end word recognition achieves state-of-the-art 86% F-Score on ICDAR13

WordFence: Text Detection in Natural Images with Border Awareness

In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a novel pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, our end-to-end word recognition system achieves state-of-the-art 86% F-Score on ICDAR13.Comment: 5 pages, 2 figures, ICIP 201

Word Free Recall Search Scales Linearly With Number of Items Recalled

I find that the total search time in word free recall, averaged over item position, increases linearly with the number of items recalled. Thus the word free recall search algorithm scales the same as the low-error recognition of integers (Sternberg, 1966). The result suggests that both simple integer recognition and the more complex word free recall use the same search algorithm. The proportionality constant of 2-4 seconds per item (a hundred times larger than for integer recognition) is a power function of the proportion not remembered and seems to be the same function for word free recall in young and old subjects, high and low presentation rates and delayed and immediate free recall. The linear scaling of the search algorithm is different from what is commonly assumed to be the word free recall search algorithm, search by random sampling. The linearity of the word free recall extends down to 3 items which presents a challenge to the prevalent working memory theory in which 3-5 items are proposed to be stored in a separate high-availability store

The scene superiority effect: object recognition in the context of natural scenes

Four experiments investigate the effect of background scene semantics on object recognition. Although past research has found that semantically consistent scene backgrounds can facilitate recognition of a target object, these claims have been challenged as the result of post-perceptual response bias rather than the perceptual processes of object recognition itself. The current study takes advantage of a paradigm from linguistic processing known as the Word Superiority Effect. Humans can better discriminate letters (e.g., D vs. K) in the context of a word (WORD vs. WORK) than in a non-word context (e.g., WROD vs. WROK) even when the context is non-predictive of the target identity. We apply this paradigm to objects in natural scenes, having subjects discriminate between objects in the context of scenes. Because the target objects were equally semantically consistent with any given scene and could appear in either semantically consistent or inconsistent contexts with equal probability, response bias could not lead to an apparent improvement in object recognition. The current study found a benefit to object recognition from semantically consistent backgrounds, and the effect appeared to be modulated by awareness of background scene semantics

Cross-linguistic activation in bilingual sentence processing: the role of word class meaning

This study investigates how categorial (word class) semantics influences cross-linguistic interactions when reading in L2. Previous homograph studies paid little attention to the possible influence of different word classes in the stimulus material on cross-linguistic activation. The present study examines the word recognition performance of Dutch-English bilinguals who performed a lexical decision task to word targets appearing in a sentence. To determine the influence of word class meaning, the critical words either showed a word class overlap (e. g. the homograph tree [ noun], which means "step" in Dutch) or not (e.g. big [ADJ], which is a noun in Dutch meaning "piglet"). In the condition of word class overlap, a facilitation effect was observed, suggesting that both languages were active. When there was no word class overlap, the facilitation effect disappeared. This result suggests that categorial meaning affects the word recognition process of bilinguals

Automatic vigilance for negative words in lexical decision and naming : comment on Larsen, Mercer, and Balota (2006)

An automatic vigilance hypothesis states that humans preferentially attend to negative stimuli, and this attention to negative valence disrupts the processing of other stimulus properties. Thus, negative words typically elicit slower color naming, word naming, and lexical decisions than neutral or positive words. Larsen, Mercer, and Balota (see record 2006-04603-006) analyzed the stimuli from 32 published studies, and they found that word valence was confounded with several lexical factors known to affect word recognition. Indeed, with these lexical factors covaried out, Larsen et al. found no evidence of automatic vigilance. The authors report a more sensitive analysis of 1011 words. Results revealed a small but reliable valence effect, such that negative words (e.g., "shark") elicit slower lexical decisions and naming than positive words (e.g., "beach"). Moreover, the relation between valence and recognition was categorical rather than linear; the extremity of a word's valence did not affect its recognition. This valence effect was not attributable to word length, frequency, orthographic neighborhood size, contextual diversity, first phoneme, or arousal. Thus, the present analysis provides the most powerful demonstration of automatic vigilance to date