Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Automated extraction of chemical structure information from digital raster images

Background: To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results: This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader -- a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion: The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links to scientific research articles.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90875/1/Saitou8.pd

Print-Scan Resilient Text Image Watermarking Based on Stroke Direction Modulation for Chinese Document Authentication

Print-scan resilient watermarking has emerged as an attractive way for document security. This paper proposes an stroke direction modulation technique for watermarking in Chinese text images. The watermark produced by the idea offers robustness to print-photocopy-scan, yet provides relatively high embedding capacity without losing the transparency. During the embedding phase, the angle of rotatable strokes are quantized to embed the bits. This requires several stages of preprocessing, including stroke generation, junction searching, rotatable stroke decision and character partition. Moreover, shuffling is applied to equalize the uneven embedding capacity. For the data detection, denoising and deskewing mechanisms are used to compensate for the distortions induced by hardcopy. Experimental results show that our technique attains high detection accuracy against distortions resulting from print-scan operations, good quality photocopies and benign attacks in accord with the future goal of soft authentication

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

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

Automatic Car Registration Plate Recognition Using the Hough Transform

The development of automatic car registration plate recognition systems will provide greater efficiency for vehicle monitoring in automatic access control, and will avoid the need to equip vehicles with special RF tags for identification since all vehicles possess a unique registration plate. Thus this study is an attempt to introduce an automatic car registration plate recognition system based on identifying the plate characters by using the Hough transform. However, the proposed recognition system can be used in conjunction with a tag system for higher security access control. The automatic registration plate recognition could also have considerable potential in a wide range of applications especially in the identification of vehicle-based offences and with law enforcement. Recent advances in computer vision technology and the falling price of the related devices has contributed in making it practical to build an automatic, registration plate recognition systems. There have been a number of Optical Character Recognition (OCR) techniques, which have been used in the recognition of car registration plate characters. These systems include the character details matching process (Lotufo, et al. 1990), BAM (Bi-directional Associative Memories) neural network (Fahmy 1994) neural network (Tindall, 1995) and cross correlation pattern matching character matching techniques (Cornelli, et al. 1995). All of these systems recognized the characters by matching the full image of every character with a character\u27s template database which requires considerable processing time and large memory for the database. The purpose of this study is to explore the potential for using Hough transform (Hough 1962) in vehicle registration plate recognition. The OCR technique used in this project is unlike the other systems where the character recognition was based on matching the character\u27s full image; However the OCR technique in this system used Hough transform to identify the characters, where the recognition of a character is based on matching its identification array to the database. To validate the research, a car registration plate recognition system was developed to locate the registration plate from the full image of a vehicle and then extrar.t the plate characters by using image processing techniques. A Hough transform algorithm was applied to every character within the registration plate image to produce an identification array for these characters, and the plate characters were recognized by matching their identification array to the database. The system has been applied to a number of video recorded car images to recognize their registration plates. The rate of correctly recognized characters was 82.7% of the extracted characters, but improvement can be granted by using a faster digital camera and taking some precautions in the registration plate frames. However, the research indicated that the optical character recognition technique used in the study is an efficient and simple algorithm to identify characters, without requiring a relatively large processing memory