Program: Automated Library and Information Systems
Fast text searching: allowing errors
Communications of the ACM
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Phonetic string matching: lessons from information retrieval
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Matching performance of binary correlation matrix memories
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
A technique for computer detection and correction of spelling errors
Communications of the ACM
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Data Mining and Knowledge Discovery
Estimating the selectivity of approximate string queries
ACM Transactions on Database Systems (TODS)
Approximate string matching by combining automaton approach and binary neural networks
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
PG-Skip: proximity graph based clustering of long strings
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
An unsupervised and data-driven approach for spell checking in Vietnamese OCR-scanned texts
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
Development of an Assamese OCR using Bangla OCR
Proceeding of the workshop on Document Analysis and Recognition
Clustering with Proximity Graphs: Exact and Efficient Algorithms
International Journal of Knowledge-Based Organizations
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In this paper, we propose a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system. We integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. In this paper, we evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost.