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| Gyeonghwan Kim, Venu Govindaraju, "A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 366-379, April, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/34.588017, author = {Gyeonghwan Kim and Venu Govindaraju}, title = {A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {19}, number = {4}, issn = {0162-8828}, year = {1997}, pages = {366-379}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.588017}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications IS - 4 SN - 0162-8828 SP366 EP379 EPD - 366-379 A1 - Gyeonghwan Kim, A1 - Venu Govindaraju, PY - 1997 KW - Handwritten word recognition KW - segmentation algorithm KW - variable duration KW - chain code representation KW - dynamic programming. VL - 19 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi.
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