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This paper describes a system being developed to recognizedate information handwritten on Canadian bankcheques. A segmentation based strategy is adopted in thissystem. In order to achieve high performances in terms ofefficiency and reliability, a knowledge-based module is proposedfor the date segmentation and a cursive month wordrecognition module is implemented based on a combinationof classifiers. The interaction between the segmentation andrecognition stages is properly established by using multi-hypothesesgeneration and evaluation modules. As a result,promising performance is obtained on a test set from a real-lifestandard cheque database.

This paper describes an off-line system which recognizes unconstrained handwritten month words extracted from Canadian bank cheques. A segmentation based grapheme level HMM (hidden Markov model) classifier and two multilayer perceptron classifiers with different architectures and different features have been developed in CENPARMI for the recognition of month words. In this paper, a combination method with an effective conditional topology is presented, and the most widely used combination rules including Vote, Sum and Product, are experimented. A new modified Product rule is also proposed, which has produced the best recognition rate of 85.36% when tested on a real-life standard Canadian bank cheque database.

Presents an approach to differentiating handwritten alphabetic words from numeric strings, which is motivated by an application in a real-life bank cheque processing system. Neural networks are first considered with the focus on effective feature extraction and architecture selection. The combination of networks is then implemented to achieve highly reliable and accurate results.

Segmenting handwritten date fields on bank cheque images into three subimages corresponding to the day, month and year is the first and critical step of our date recognition system. The paper describes a knowledge-based segmentation system, which introduces different kinds of knowledge at different segmentation stages to improve the performance. The knowledge includes information on the writing style, syntactic and semantic constraints, etc. Results have shown that the system is very effective compared with a previous structural feature based method.

The last frontiers of handwriting recognition are considered to have started in the last decade of the second millennium. The paper summarizes (a) the nature of the problem of handwriting recognition, (b) the state of the art of handwriting recognition at the turn of the new millennium, and (c) the results of CENPARMI researchers in automatic recognition of handwritten digits, touching numerals, cursive scripts, and dates formed by a mixture of the former 3 categories. Wherever possible, comparable results have been tabulated according to techniques used, databases, and performance. Aspects related to human generation and perception of handwriting are discussed. The extraction and usage of human knowledge, and their incorporation into handwriting recognition systems are presented. Challenges, aims, trends, efforts and possible rewards, and suggestions for future investigations are also included.