Design and Implementation Recognition System for Handwritten Hindi/Marathi Document

In the present scenario most of the importance is given for the “paperless office” there by more and more communication and storage of documents is performed digitally. Documents and files which are present in Hindi and Marathi languages that were once stored physically on paper are now being converted into electronic form in order to facilitate quicker additions, searches, and modifications, as well as to prolong the life of such records. Because of this, there is a great demand of such software, which automatically extracts, analyze, recognize and store information from physical documents for later retrieval. Skew detection is used for text line position determination in Digitized documents, automated page orientation, and skew angle detection for binary document images, skew detection in handwritten scripts, in compensation for Internet audio applications and in the correction of scanned documents

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Generating an Arabic Calligraphy Text Blocks for Global Texture Analysis

This paper objective is to improve the current method for generating an Arabic Calligraphy text blocks. We test on seven types of Arabic Calligraphy text. We apply  projection profiles and a proposed filter to discriminate each line of the Arabic Calligraphy scripts. After performing text detection, skew correction, text and line normalization subsequently, we generate Arabic Calligraphy text blocks for global texture analysis purposes. We compare our proposed filter with current method and median filter. The results show that the proposed filter  is outperformed. The proposed method can be further  improved to boost the overall performance

Computer analysis of composite documents with non-uniform background.

The motivation behind most of the applications of off-line text recognition is to convert data from conventional media into electronic media. Such applications are bank cheques, security documents and form processing. In this dissertation a document analysis system is presented to transfer gray level composite documents with complex backgrounds and poor illumination into electronic format that is suitable for efficient storage, retrieval and interpretation. The preprocessing stage for the document analysis system requires the conversion of a paper-based document to a digital bit-map representation after optical scanning followed by techniques of thresholding, skew detection, page segmentation and Optical Character Recognition (OCR). The system as a whole operates in a pipeline fashion where each stage or process passes its output to the next stage. The success of each stage guarantees that the operation of the system as a whole with no failures that may reduce the character recognition rate. By designing this document analysis system a new local bi-level threshold selection technique was developed for gray level composite document images with non-uniform background. The algorithm uses statistical and textural feature measures to obtain a feature vector for each pixel from a window of size (2 n + 1) x (2n + 1), where n ≥ 1. These features provide a local understanding of pixels from their neighbourhoods making it easier to classify each pixel into its proper class. A Multi-Layer Perceptron Neural Network is then used to classify each pixel value in the image. The results of thresholding are then passed to the block segmentation stage. The block segmentation technique developed is a feature-based method that uses a Neural Network classifier to automatically segment and classify the image contents into text and halftone images. Finally, the text blocks are passed into a Character Recognition (CR) system to transfer characters into an editable text format and the recognition results were compared to those obtained from a commercial OCR. The OCR system implemented uses pixel distribution as features extracted from different zones of the characters. A correlation classifier is used to recognize the characters. For the application of cheque processing, this system was used to read the special numerals of the optical barcode found in bank cheques. The OCR system uses a fuzzy descriptive feature extraction method with a correlation classifier to recognize these special numerals, which identify the bank institute and provides personal information about the account holder. The new local thresholding scheme was tested on a variety of composite document images with complex backgrounds. The results were very good compared to the results from commercial OCR software. This proposed thresholding technique is not limited to a specific application. It can be used on a variety of document images with complex backgrounds and can be implemented in any document analysis system provided that sufficient training is performed.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .A445. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1061. Advisers: Maher Sid-Ahmed; Majid Ahmadi. Thesis (Ph.D.)--University of Windsor (Canada), 2004