A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
Lexicon-Driven Handwritten Word Recognition Using Optimal Linear Combinations of Order Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rejection strategies for offline handwritten text line recognition
Pattern Recognition Letters
Rejection strategies for offline handwritten text line recognition
Pattern Recognition Letters
| Hi-index | 0.00 |
This paper discusses the relative merits and complexities of two word recognition algorithms: lexicon directed and lexicon free techniques. This algorithm operates on a pre-segmented word image and yields the optimum concatenation of the image segments for each word in the lexicon. However, the computational complexity of this algorithm is quite high, as the optimum concatenation is required for every word in the lexicon. In the lexicon free word matching process, the character likelihood for all the letters are calculated and the maximum likelihood value and the associated letter are determined. In this approach an optimum string results from the concatenation process. The word matching process is applied only once for an input word image. Comparative results with regard to accuracy, speed and size of lexicon are presented.