An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with theComment: 6pages, 5 figure

RAPID ANALYTICAL VERIFICATION OF HANDWRITTEN ALPHANUMERIC ADDRESS FIELDS

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF This paper presents a combination of fuzzy system and dynamic analytical model to deal with imprecise data derived from feature extraction in handwritten address images which are compared against postulated addresses for address verification. A dynamic building­number locator is able to locate and recognise the building­number, without knowing exactly where the building­number starts in the candidate address line. The overall system achieved a correct sorting rate of 72.9%, 27.1% rejection rate and 0.0% error rate on a blind test set of 450 cursive handwritten addresses.

An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length

Some Approaches to the Recognition of Handwritten Numerals

The present work deals with the recognition of handwritten isolated numerals by utilizing a recent approach, which aims at tackling the variability in the writing styles. A two pronged approach involving pre-classification and the recognition has been followed in this paper. For the pre-classification of numerals, two approaches have been presented. The first is heuristic based and the second is stroke based. A recent feature extraction method, namely sector data method, which takes care of variability in the handwritten numerals, has been incorporated into the system thus the variability involved in the writing styles of different individuals is taken care of by extracting features from the sector based approach. The back propagation neural networks have been used in the recognition process using the features extracted from the sector-based approach. On the basis of recognition rates obtained with samples written by different individuals, it is concluded that the sector based approach is better suited for the recognition of numerals when pre-classification is made on the basis of strokes

A Knowledge based segmentation algorithm for enhanced recognition of handwritten courtesy amounts

"March 1994."Includes bibliographical references (p. [23]-[24]).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Karim Hussein ... [et al.

Multi-experts for touching digit string recognition

84.6 % of touching digit strings have only two digits touching, 12.3 % have three digits touching, and 3.1% have more than three digits touching. We present a multiexperts approach to recognize touching digit pairs (TDP) and touching digit triples (TDT). We combine holistic and traditional segmentation methods. 25,686 TDP training samples and 2778 TDP testing samples collected from USPS mail are used in our experiment. Holistic method outperforms the traditional segmentation based methods. The multi-experts combination has the best performance, a correct rate of 91.1 % on TDP. 1