An Integrated architecture for recognition of totally unconstrained handwritten numerals

Reprint. Reprinted from the International journal of pattern recognition and artificial intelligence. Vol. 7, no. 4 (1993) "January 1993."Includes bibliographical references (p. 127-128).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Amar Gupta ... [et al.

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.

Reconocimiento de Números Manuscritos

En la actualidad, el reconocimiento de texto manuscrito sigue siendo una fuente de intensa investigación. Este paper presenta una herramienta de software perteneciente al área de Reconocimiento Inteligente de Caracteres (ICR – Intelligent Character Recognition) para el reconocimiento de números enteros manuscritos. En ella se integra un clasificador basado en redes neuronales feedforward y un conjunto de técnicas pertenecientes al área de procesamiento de imágenes digitales que realiza las adaptaciones adecuadas sobre la imagen de entrada. De esta forma, se ingresa un número entero manuscrito formado por varios dígitos y se obtiene como resultado el reconocimiento de cada uno de los elementos que lo componen. Los resultados de la aplicación de esta herramienta sobre una base de números del repositorio UCI han sido satisfactorios. Es importante destacar que, si bien los resultados expuestos en este artículo se refieren exclusivamente al reconocimiento de números manuscritos, esta herramienta puede ser aplicada al conjunto de caracteres completo. Finalmente se incluyen algunas conclusiones así como algunas líneas de trabajo futuras.At present, handwritten text recognition still represents a wide source of research. This paper presents a software tool which belongs to the area of ICR (Intelligent Character Recognition) for the recognition of handwritten integers. A classifier based on feedforward neural networks and a set of techniques belonging to digital image processing area are incorporated to this tool, which make the suitable adaptations over the input image. In this way, a handwritten integer made up by several digits is entered and, as a result, the recognition of each of its elements is obtained. The results of applying this tool over a UCI repository number base have been successful. It is important to notice that, even though the results presented in this paper exclusively refer to handwritten number recognition, this tool can be applied to the complete set of characters. Finally, some conclusions are presented together with some future lines of work.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Reconocimiento de Números Manuscritos

En la actualidad, el reconocimiento de texto manuscrito sigue siendo una fuente de intensa investigación. Este paper presenta una herramienta de software perteneciente al área de Reconocimiento Inteligente de Caracteres (ICR – Intelligent Character Recognition) para el reconocimiento de números enteros manuscritos. En ella se integra un clasificador basado en redes neuronales feedforward y un conjunto de técnicas pertenecientes al área de procesamiento de imágenes digitales que realiza las adaptaciones adecuadas sobre la imagen de entrada. De esta forma, se ingresa un número entero manuscrito formado por varios dígitos y se obtiene como resultado el reconocimiento de cada uno de los elementos que lo componen. Los resultados de la aplicación de esta herramienta sobre una base de números del repositorio UCI han sido satisfactorios. Es importante destacar que, si bien los resultados expuestos en este artículo se refieren exclusivamente al reconocimiento de números manuscritos, esta herramienta puede ser aplicada al conjunto de caracteres completo. Finalmente se incluyen algunas conclusiones así como algunas líneas de trabajo futuras.At present, handwritten text recognition still represents a wide source of research. This paper presents a software tool which belongs to the area of ICR (Intelligent Character Recognition) for the recognition of handwritten integers. A classifier based on feedforward neural networks and a set of techniques belonging to digital image processing area are incorporated to this tool, which make the suitable adaptations over the input image. In this way, a handwritten integer made up by several digits is entered and, as a result, the recognition of each of its elements is obtained. The results of applying this tool over a UCI repository number base have been successful. It is important to notice that, even though the results presented in this paper exclusively refer to handwritten number recognition, this tool can be applied to the complete set of characters. Finally, some conclusions are presented together with some future lines of work.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Reconocimiento de Números Manuscritos

En la actualidad, el reconocimiento de texto manuscrito sigue siendo una fuente de intensa investigación. Este paper presenta una herramienta de software perteneciente al área de Reconocimiento Inteligente de Caracteres (ICR – Intelligent Character Recognition) para el reconocimiento de números enteros manuscritos. En ella se integra un clasificador basado en redes neuronales feedforward y un conjunto de técnicas pertenecientes al área de procesamiento de imágenes digitales que realiza las adaptaciones adecuadas sobre la imagen de entrada. De esta forma, se ingresa un número entero manuscrito formado por varios dígitos y se obtiene como resultado el reconocimiento de cada uno de los elementos que lo componen. Los resultados de la aplicación de esta herramienta sobre una base de números del repositorio UCI han sido satisfactorios. Es importante destacar que, si bien los resultados expuestos en este artículo se refieren exclusivamente al reconocimiento de números manuscritos, esta herramienta puede ser aplicada al conjunto de caracteres completo. Finalmente se incluyen algunas conclusiones así como algunas líneas de trabajo futuras.At present, handwritten text recognition still represents a wide source of research. This paper presents a software tool which belongs to the area of ICR (Intelligent Character Recognition) for the recognition of handwritten integers. A classifier based on feedforward neural networks and a set of techniques belonging to digital image processing area are incorporated to this tool, which make the suitable adaptations over the input image. In this way, a handwritten integer made up by several digits is entered and, as a result, the recognition of each of its elements is obtained. The results of applying this tool over a UCI repository number base have been successful. It is important to notice that, even though the results presented in this paper exclusively refer to handwritten number recognition, this tool can be applied to the complete set of characters. Finally, some conclusions are presented together with some future lines of work.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informátic

Using generative models for handwritten digit recognition

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques

An Integrated architecture for recognition of totally unconstrained handwritten numerals

Reprint. Reprinted from the International journal of pattern recognition and artificial intelligence. Vol. 7, no. 4 (1993) "January 1993."HD28 .M414 no.3765-, 95,

ICheck--an architecture for secure transactions in the processing of bank checks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 96-97).by Joseph Figueroa.M.Eng

Arabic text recognition of printed manuscripts. Efficient recognition of off-line printed Arabic text using Hidden Markov Models, Bigram Statistical Language Model, and post-processing.

Arabic text recognition was not researched as thoroughly as other natural languages. The need for automatic Arabic text recognition is clear. In addition to the traditional applications like postal address reading, check verification in banks, and office automation, there is a large interest in searching scanned documents that are available on the internet and for searching handwritten manuscripts. Other possible applications are building digital libraries, recognizing text on digitized maps, recognizing vehicle license plates, using it as first phase in text readers for visually impaired people and understanding filled forms. This research work aims to contribute to the current research in the field of optical character recognition (OCR) of printed Arabic text by developing novel techniques and schemes to advance the performance of the state of the art Arabic OCR systems. Statistical and analytical analysis for Arabic Text was carried out to estimate the probabilities of occurrences of Arabic character for use with Hidden Markov models (HMM) and other techniques. Since there is no publicly available dataset for printed Arabic text for recognition purposes it was decided to create one. In addition, a minimal Arabic script is proposed. The proposed script contains all basic shapes of Arabic letters. The script provides efficient representation for Arabic text in terms of effort and time. Based on the success of using HMM for speech and text recognition, the use of HMM for the automatic recognition of Arabic text was investigated. The HMM technique adapts to noise and font variations and does not require word or character segmentation of Arabic line images. In the feature extraction phase, experiments were conducted with a number of different features to investigate their suitability for HMM. Finally, a novel set of features, which resulted in high recognition rates for different fonts, was selected. The developed techniques do not need word or character segmentation before the classification phase as segmentation is a byproduct of recognition. This seems to be the most advantageous feature of using HMM for Arabic text as segmentation tends to produce errors which are usually propagated to the classification phase. Eight different Arabic fonts were used in the classification phase. The recognition rates were in the range from 98% to 99.9% depending on the used fonts. As far as we know, these are new results in their context. Moreover, the proposed technique could be used for other languages. A proof-of-concept experiment was conducted on English characters with a recognition rate of 98.9% using the same HMM setup. The same techniques where conducted on Bangla characters with a recognition rate above 95%. Moreover, the recognition of printed Arabic text with multi-fonts was also conducted using the same technique. Fonts were categorized into different groups. New high recognition results were achieved. To enhance the recognition rate further, a post-processing module was developed to correct the OCR output through character level post-processing and word level post-processing. The use of this module increased the accuracy of the recognition rate by more than 1%.King Fahd University of Petroleum and Minerals (KFUPM