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

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

Computer vision reading on stickers and direct part marking on horticultural products : challenges and possible solutions

Traceability of products from production to the consumer has led to a technological advancement in product identification. There has been development from the use of traditional one-dimensional barcodes (EAN-13, Code 128, etc.) to 2D (two-dimensional) barcodes such as QR (Quick Response) and Data Matrix codes. Over the last two decades there has been an increased use of Radio Frequency Identification (RFID) and Direct Part Marking (DPM) using lasers for product identification in agriculture. However, in agriculture there are still considerable challenges to adopting barcodes, RFID and DPM technologies, unlike in industry where these technologies have been very successful. This study was divided into three main objectives. Firstly, determination of the effect of speed, dirt, moisture and bar width on barcode detection was carried out both in the laboratory and a flower producing company, Brandkamp GmbH. This study developed algorithms for automation and detection of Code 128 barcodes under rough production conditions. Secondly, investigations were carried out on the effect of low laser marking energy on barcode size, print growth, colour and contrast on decoding 2D Data Matrix codes printed directly on apples. Three different apple varieties (Golden Delicious, Kanzi and Red Jonaprince) were marked with various levels of energy and different barcode sizes. Image processing using Halcon 11.0.1 (MvTec) was used to evaluate the markings on the apples. Finally, the third objective was to evaluate both algorithms for 1D and 2D barcodes. According to the results, increasing the speed and angle of inclination of the barcode decreased barcode recognition. Also, increasing the dirt on the surface of the barcode resulted in decreasing the successful detection of those barcodes. However, there was 100% detection of the Code 128 barcode at the company’s production speed (0.15 m/s) with the proposed algorithm. Overall, the results from the company showed that the image-based system has a future prospect for automation in horticultural production systems. It overcomes the problem of using laser barcode readers. The results for apples showed that laser energy, barcode size, print growth, type of product, contrast between the markings and the colour of the products, the inertia of the laser system and the days of storage all singularly or in combination with each other influence the readability of laser Data Matrix codes and implementation on apples. There was poor detection of the Data Matrix code on Kanzi and Red Jonaprince due to the poor contrast between the markings on their skins. The proposed algorithm is currently working successfully on Golden Delicious with 100% detection for 10 days using energy 0.108 J mm-2 and a barcode size of 10 × 10 mm2. This shows that there is a future prospect of not only marking barcodes on apples but also on other agricultural products for real time production

A general approach for multi-oriented text line extraction of handwritten document

International audienceThe multi-orientation occurs frequently in ancient handwritten documents, where the writers try to update a document by adding some annotations in the margins. Due to the margin narrowness, this gives rise to lines in different directions and orientations. Document recognition needs to find the lines everywhere they are written whatever their orientation. This is why we propose in this paper a new approach allowing us to extract the multi-oriented lines in scanned documents. Because of the multi-orientation of lines and their dispersion in the page, we use an image meshing allowing us to progressively and locally determine the lines. Once the meshing is established, the orientation is determined using the Wigner-Ville distribution on the projection histogram profile. This local orientation is then enlarged to limit the orientation in the neighborhood. Afterward, the text lines are extracted locally in each zone basing on the follow-up of the orientation lines and the proximity of connected components. Finally, the connected components that overlap and touch in adjacent lines are separated. The morphology analysis of the terminal letters of Arabic words is here considered. The proposed approach has been experimented on 100 documents reaching an accuracy of about 98.6

Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

Text Segmentation in Web Images Using Colour Perception and Topological Features

The research presented in this thesis addresses the problem of Text Segmentation in Web images. Text is routinely created in image form (headers, banners etc.) on Web pages, as an attempt to overcome the stylistic limitations of HTML. This text however, has a potentially high semantic value in terms of indexing and searching for the corresponding Web pages. As current search engine technology does not allow for text extraction and recognition in images, the text in image form is ignored. Moreover, it is desirable to obtain a uniform representation of all visible text of a Web page (for applications such as voice browsing or automated content analysis). This thesis presents two methods for text segmentation in Web images using colour perception and topological features. The nature of Web images and the implicit problems to text segmentation are described, and a study is performed to assess the magnitude of the problem and establish the need for automated text segmentation methods. Two segmentation methods are subsequently presented: the Split-and-Merge segmentation method and the Fuzzy segmentation method. Although approached in a distinctly different way in each method, the safe assumption that a human being should be able to read the text in any given Web Image is the foundation of both methods’ reasoning. This anthropocentric character of the methods along with the use of topological features of connected components, comprise the underlying working principles of the methods. An approach for classifying the connected components resulting from the segmentation methods as either characters or parts of the background is also presented