Fast dictionary look-up for contextual word recognition

Wells, C. J.; Evett, L; Whitby, P. E.; Whitrow, R.J.

doi:10.1016/0031-3203(90)90071-r

Cited by 34 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Error correction methods use algorithms for approximate or exact text matching [e.g., 1,7,12,13,15,17]. Three temporal points of error identification and correction in text entry are error prevention, automatic error correction, and user-initiated spell checking.…”

Section: Error Correctionmentioning

confidence: 99%

Eyes-free text entry with error correction on touchscreen mobile devices

Tinwala

MacKenzie

2010

Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries

View full text Add to dashboard Cite

We present an eyes-free text entry method for mobile touchscreen devices. Input progresses by inking Graffiti strokes using a finger on a touchscreen. The system includes a word-level error correction algorithm. Auditory and tactile feedback guide eyes-free entry using speech and non-speech sounds, and by vibrations. In a study with 12 participants, three different feedback modes were tested. Entry speed, accuracy, and algorithm performance were compared between the three feedback modes. An overall entry speed of 10.0 wpm was found with a maximum rate of 21.5 wpm using a feedback mode that required a recognized stroke at the beginning of each word. Text was entered with an overall accuracy of 95.7%. The error correction algorithm performed well: 14.9% of entered text was corrected on average, representing a 70.3% decrease in errors compared to no algorithm. Where multiple candidates appeared, the intended word was 1 st or 2 nd in the list 94.2% of the time.

show abstract

Section: Error Correctionmentioning

confidence: 99%

Eyes-free text entry with error correction on touchscreen mobile devices

Tinwala

MacKenzie

2010

Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries

View full text Add to dashboard Cite

show abstract

“…Text blocks are usually processed and recognized using optical character recognition (OCR) engines. The objective of test recognition [28,29] is not only to recognize individual characters of a word, but also to verify that the word is valid. To achieve this, it is necessary to carry out contextual postprocessing which improves recognition accuracy by using a large dictionary.…”

Section: Document Image Processingmentioning

confidence: 99%

Distributed fax message processing system

Hui

Chan

Guo

1997

Journal of Network and Computer Applications

View full text Add to dashboard Cite

“…Our studies mainly apply two competitive dictionary data structures-hash tables [8] and letter trees (tries) [2,5,18]-combining inherent advantages of both. The dictionary kernel, however, is based on hashing.…”

Section: Dictionary Organization and Accessmentioning

confidence: 99%

On Virtual Partitioning of Large Dictionaries for Contextual Post-Processing to Improve Character Recognition

Hoch

Kieninger

1996

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

This article presents a new approach to the partitioning of large dictionaries by virtual views. The basic idea is that additional knowledge sources of text recognition and text analysis are employed for fast dictionary look-up in order to prune search space through static or dynamic views. The heart of the system is a redundant hashing technique which involves a set of hash functions dealing with noisy input efficiently. Currently, the system is composed of two main system components: the dictionary generator and the dictionary controller. While the dictionary generator initially builds the system by using profiles and source dictionaries, the controller allows the flexible integration of different search heuristics. Results prove that our system achieves a significant speed-up of dictionary access time.

show abstract

Fast dictionary look-up for contextual word recognition

Cited by 34 publications

References 14 publications

Eyes-free text entry with error correction on touchscreen mobile devices

Eyes-free text entry with error correction on touchscreen mobile devices

Distributed fax message processing system

On Virtual Partitioning of Large Dictionaries for Contextual Post-Processing to Improve Character Recognition

Contact Info

Product

Resources

About