Statistics Oriented Preprocessing of Document Image

Old printed documents represent an important part of our cultural heritage. Their digitalization plays an important role in creating data and metadata. The paper proposed an algorithm for estimation of the global text skew. First, document image is binarized reducing the impact of noise and uneven illumination. The binary image is statistically analyzed and processed. Accordingly, redundant data have been excluded. Furthermore, the convex hulls are established encircling each text object. They are joined establishing connected components. Then, the connected components in complementary image are enlarged with morphological dilation. At the end, the biggest connected component is extracted. Its orientation is similar to the global orientation of text document which is calculated by the moments. Efficiency and correctness of the algorithm are verified by testing on a custom dataset

PUBLIC OCR SIGN AGE RECOGNITION WITH SKEW & SLANT CORRECTION FOR VISUALLY IMP AIRED PEOPLE

This paper presents an OCR hybrid recognition model for the Visually Impaired People (VIP). The VIP often encounters problems navigating around independently because they are blind or have poor vision. They are always being discriminated due to their limitation which can lead to depression to the VIP. Thus, they require an efficient technological assistance to help them in their daily activity. The objective of this paper is to propose a hybrid model for Optical Character Recognition (OCR) to detect and correct skewed and slanted character of public signage. The proposed hybrid model should be able to integrate with speech synthesizer for VIP signage recognition. The proposed hybrid model will capture an image of a public signage to be converted into machine readable text in a text file. The text will then be read by a speech synthesizer and translated to voice as the output. In the paper, hybrid model which consist of Canny Method, Hough Transformation and Shearing Transformation are used to detect and correct skewed and slanted images. An experiment was conducted to test the hybrid model performance on 5 blind folded subjects. The OCR hybrid recognition model has successfully achieved a Recognition Rate (RR) of 82. 7%. This concept of public signage recognition is being proven by the proposed hybrid model which integrates OCR and speech synthesizer

Estimation of the Text Skew in the Old Printed Documents

Old printed documents represent the significant part of our heritage. In order to preserve them, the digitalization is indispensable. The paper proposed a robust skew estimation method for old printed document. It is based on the connected components made by filled convex hulls around text element. The connected components are enlarged by oriented morphological operation. Then, the longest connected component is extracted. The global orientation of the document is detected by its orientation. Accordingly, document image was globally de-skewed. The algorithm is tested on synthetic and real datasets. Obtained results proved the algorithmscorrectness

PUBLIC OCR SIGN AGE RECOGNITION WITH SKEW & SLANT CORRECTION FOR VISUALLY IMP AIRED PEOPLE

This paper presents an OCR hybrid recognition model for the Visually Impaired People (VIP). The VIP often encounters problems navigating around independently because they are blind or have poor vision. They are always being discriminated due to their limitation which can lead to depression to the VIP. Thus, they require an efficient technological assistance to help them in their daily activity. The objective of this paper is to propose a hybrid model for Optical Character Recognition (OCR) to detect and correct skewed and slanted character of public signage. The proposed hybrid model should be able to integrate with speech synthesizer for VIP signage recognition. The proposed hybrid model will capture an image of a public signage to be converted into machine readable text in a text file. The text will then be read by a speech synthesizer and translated to voice as the output. In the paper, hybrid model which consist of Canny Method, Hough Transformation and Shearing Transformation are used to detect and correct skewed and slanted images. An experiment was conducted to test the hybrid model performance on 5 blind folded subjects. The OCR hybrid recognition model has successfully achieved a Recognition Rate (RR) of 82. 7%. This concept of public signage recognition is being proven by the proposed hybrid model which integrates OCR and speech synthesizer

PUBLIC OCR SIGN AGE RECOGNITION WITH SKEW & SLANT CORRECTION FOR VISUALLY IMP AIRED PEOPLE

This paper presents an OCR hybrid recognition model for the Visually Impaired People (VIP). The VIP often encounters problems navigating around independently because they are blind or have poor vision. They are always being discriminated due to their limitation which can lead to depression to the VIP. Thus, they require an efficient technological assistance to help them in their daily activity. The objective of this paper is to propose a hybrid model for Optical Character Recognition (OCR) to detect and correct skewed and slanted character of public signage. The proposed hybrid model should be able to integrate with speech synthesizer for VIP signage recognition. The proposed hybrid model will capture an image of a public signage to be converted into machine readable text in a text file. The text will then be read by a speech synthesizer and translated to voice as the output. In the paper, hybrid model which consist of Canny Method, Hough Transformation and Shearing Transformation are used to detect and correct skewed and slanted images. An experiment was conducted to test the hybrid model performance on 5 blind folded subjects. The OCR hybrid recognition model has successfully achieved a Recognition Rate (RR) of 82. 7%. This concept of public signage recognition is being proven by the proposed hybrid model which integrates OCR and speech synthesizer

Detecção de Inclinação em Imagens de Documentos

A digitalização de documentos contribui para a preservação da informação evitando sua perda devido à degradação física do papel. Atualmente, Sistemas de Reconhecimento Automático de Imagens de Documentos são empregados para converter, automaticamente, a informação contida nas imagens em texto editável, de forma rápida e sem a necessidade da presença de um indivíduo. Assim, tornando essa informação pesquisável através, por exemplo, de palavras-chave.A inclinação em documentos é um problema freqüente nesses sistemas e, em geral, é  imposta durante a digitalização, quando o papel é posicionado com um ângulo diferente de zero grau sobre o eixo do scanner. No caso de documentos manuscritos, a inclinação pode surgir durante a escrita do próprio documento, principalmente quando o escritor não tem uma linha de pauta como guia. A correção da inclinação é essencial para o bom desempenho de sistemas de reconhecimento automático.Este trabalho aborda o problema da detecção de inclinação em documentos impressos e manuscritos, trazendo uma revisão dos principais métodos para detecção de inclinação divulgados na literatura até os dias atuais. As principais técnicas são expostas de forma categorizada e vantagens e limitações de cada método são discutidas