Brazilian Bank Checks Research Articles

Thresholding the Courtesy Amount of Brazilian Bank Checks Based on Tsallis Entropy

Automatic bank check processing is a very hard task as it requires efficiency and precision. Banks have been adding different background patterns in the checks to increase its security. For automatic recognition of the contents of the check, the systems need to remove this background as it does not contain any valid information of the check. This can be done by thresholding algorithms which can work in document images removing all the background and letting just the ink colors to remain. In this paper, we present a new algorithm to threshold images of the courtesy amount of Brazilian bank checks. This new algorithm uses the concept of entropy as defined by Tsallis to find the best cut-off value. Experiments have shown that our algorithm is better than several well-known thresholding algorithms.

Methodology for the design of NN-based month-word recognizers written on Brazilian bank checks

The study of handwritten words is tied to the development of recognition methods to be used in real-world applications involving handwritten words, such as bank checks, postal envelopes, and handwritten texts, among others. In this work, the focus is handwritten words in the context of Brazilian bank checks, specifically the months of the year, and no restrictions are placed on the types or styles of writing or the number of writers. A global feature set and two architectures of artificial neural networks (ANN) are evaluated for classification of the words. The objectives are to evaluate the performance of conventional and class-modular multiple-layer perceptron (MLP) architectures, to develop a rejection mechanism based on multiple thresholds, and to analyze the behavior of the feature set proposed in the two architectures. The experimental results demonstrate the superiority of the class-modular architecture over the conventional MLP architecture. A rejection mechanism with multiple thresholds demonstrates favorable performance in both architectures. The feature set analysis shows the importance of the structural primitives such as concavities and convexities, and perceptual primitives such as ascenders and descenders. The experimental results reveal a recognition rate of 81.75% without the rejection mechanism, and a reliability rate 91.52% with a rejection rate of 25.33%.

Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach

This paper presents an HMM-MLP hybrid system for segmenting and recognizing complex date images written on Brazilian bank checks. Through the recognition process, the system makes use of an HMM-based approach to segment a date image into subfields. Then the three obligatory date subfields (day, month, and year) are processed. A neural approach has been adopted to decipher strings of digits (day and year) and a Markovian strategy to recognize and verify words (month). The final decision module makes an accept/reject decision. We also introduce the concept of metaclasses of digits to reduce the lexicon size of the day and year and improve the precision of their segmentation and recognition. Experiments show interesting results on date recognition.

Automatic recognition of handwritten numerical strings: a recognition and verification strategy

A modular system to recognize handwritten numerical strings is proposed. It uses a segmentation-based recognition approach and a recognition and verification strategy. The approach combines the outputs from different levels such as segmentation, recognition, and postprocessing in a probabilistic model. A new verification scheme which contains two verifiers to deal with the problems of oversegmentation and undersegmentation is presented. A new feature set is also introduced to feed the oversegmentation verifier. A postprocessor based on a deterministic automaton is used and the global decision module makes an accept/reject decision. Finally, experimental results on two databases are presented: numerical amounts on Brazilian bank checks and NIST SD19. The latter aims at validating the concept of modular system and showing the robustness of the system using a well-known database.