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

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 A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition.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

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Jumlah Transisi Pada Ciri Transisi Dalam Pengenalan Pola Tulisan Tangan Aksara Jawa Nglegeno Dengan Multiclass Support Vector Machines

Feature extraction is one of the most improtant step on characters recognition system. Transition features is one from many features used on characters recognition system. This paper report a research on handwritten basic Jawanesse characters recognition system to found the proper numbers of transitions used on transition features. To recognize the characters,the Multiclass Support Vector Machines were used. The Directed Acyclic Graph (DAG) SVM were used for multiclass classification strategy and to map each input vector to a higher dimention space, the Gaussian Radial Basis Function (RBF) kernel with parameter 1were used. It can be shown, for basicJawanesse characters recognition system, the optimal numbers of transitions used for transition features is 4 (a half of maximum numbers of transition on all patterns)

Pengaruh Variasi Jumlah Data Pelatihan SVM Terhadap Unjukkerja Pada Sistem Pengenalan Pola Tulisan Tangan Aksara Jawa Nglegeno

Support Vector Machines (SVM) is one of many classification methods implemented on pattern recognition, including handwritten character recognition system. Numbers of traning set will affect the performance of the recognition system. A basic Jawanessehandwritten character recognition system based on SVM used to show the effect of training set numbers variation on system performance. The experiment result indicating that SVM has good generalization on limiting training set (on a data set, 30% data used for training set can give more than 90% success rate on all data set)