On partitioning a dictionary for visual text recognition

Sinha, R.M.K.

doi:10.1016/0031-3203(90)90070-2

Cited by 25 publications

(13 citation statements)

References 5 publications

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“…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

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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.

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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

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“…Seminal and comprehensive work in DOCR is carried out by R.M.K. Sinha and V. Bansal, [1][2][3][4][5][6][7]. A general Review of Statistical Pattern Recognition can also be found in [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Review of Research on Devnagari Character Recognition

Dongre¹,

Mankar²,

Suganya³

2010

IJCA

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English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. Rapidly growing computational power may enable the implementation of Indic CR methodologies. Digital document processing is gaining popularity for application to office and library automation, bank and postal services, publishing houses and communication technology. Devnagari being the national language of India, spoken by more than 500 million people, should be given special attention so that document retrieval and analysis of rich ancient and modern Indian literature can be effectively done. This article is intended to serve as a guide and update for the readers, working in the Devnagari Optical Character Recognition (DOCR) area. An overview of DOCR systems is presented and the available DOCR techniques are reviewed. The current status of DOCR is discussed and directions for future research are suggested.

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“…By using an augmented dictionary and ignoring punctuation, (Sinha and Prasada, 1988) achieved 97% of word recognition with a Viterbi type algorithm. In the context of text recognition, certain possible way of partitioning a dictionary is proposed by (Sinha, 1990). The word-length, word-envelop and character combination is used for the basis of partitioning the dictionary.…”

Section: Dictionary Based Spelling Correctionmentioning

confidence: 99%