Ocr: A Statistical Model Of Multi-engine Ocr Systems

This thesis is a benchmark performed on three commercial Optical Character Recognition (OCR) engines. The purpose of this benchmark is to characterize the performance of the OCR engines with emphasis on the correlation of errors between each engine. The benchmarks are performed for the evaluation of the effect of a multi-OCR system employing a voting scheme to increase overall recognition accuracy. This is desirable since currently OCR systems are still unable to recognize characters with 100% accuracy. The existing error rates of OCR engines pose a major problem for applications where a single error can possibly effect significant outcomes, such as in legal applications. The results obtained from this benchmark are the primary determining factor in the decision of implementing a voting scheme. The experiment performed displayed a very high accuracy rate for each of these commercial OCR engines. The average accuracy rate found for each engine was near 99.5% based on a less than 6,000 word document. While these error rates are very low, the goal is 100% accuracy in legal applications. Based on the work in this thesis, it has been determined that a simple voting scheme will help to improve the accuracy rate

Hybrid model of post-processing techniques for Arabic optical character recognition

Optical character recognition (OCR) is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literatures are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks. These techniques were later combined into a hybrid model that can recognize the optical characters in the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. The performance measurements used in this study were Word Error Rate (WER), Character Error Rate (CER), and Non-word Error Rate (NWER). Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques. Similarly, the hybrid model also obtained lower WER, CER, and NWER by 30.35%, 52.42%, and 47.86% respectively when compared to the three relevant existing models. This study contributes to the OCR domain as the proposed hybrid model of post-processing techniques could facilitate the automatic recognition of Arabic text. Hence, it will lead to a better information retrieval

Parametric classification in domains of characters, numerals, punctuation, typefaces and image qualities

This thesis contributes to the Optical Font Recognition problem (OFR), by developing a classifier system to differentiate ten typefaces using a single English character ‘e’. First, features which need to be used in the classifier system are carefully selected after a thorough typographical study of global font features and previous related experiments. These features have been modeled by multivariate normal laws in order to use parameter estimation in learning. Then, the classifier system is built up on six independent schemes, each performing typeface classification using a different method. The results have shown a remarkable performance in the field of font recognition. Finally, the classifiers have been implemented on Lowercase characters, Uppercase characters, Digits, Punctuation and also on Degraded Images

Adaptive restoration of text images containing touching and broken characters

For document processing systems, automated data entry is generally performed by optical character recognition (OCR) systems. To make these systems practical, reliable OCR systems are essential. However, distortions in document images cause character recognition errors, thereby, reducing the accuracy of OCR systems. In document images, most OCR errors are caused by broken and touching characters. This thesis presents an adaptive system to restore text images distorted by touching and broken characters. The adaptive system uses the distorted text image and the output from an OCR system to generate the training character image. Using the training image and the distorted image, the system trains an adaptive restoration filter and then uses the trained filter to restore the distorted text image. To demonstrate the performance of this technique, it was applied to several distorted images containing touching or broken characters. The results show that this technique can improve both pixel and OCR accuracy of distorted text images containing touching or broken characters

Arabic Typed Text Recognition in Graphics Images (ATTR-GI)

While optical character recognition (OCR) techniques may perform well on standard text documents, their performance degrades significantly in graphics images. In standard scanned text documents OCR techniques enjoy a number of convenient assumptions such as clear backgrounds, standard fonts, predefined line orientation, page size, the start point of written. These assumptions are not true in graphics documents such as Arabic advertisements, personal cards, screenshot. Therefore, in such types of images, greater attention is required in the initial stage of detecting Arabic text regions in order for subsequent character recognition steps to be successful. Special features of Arabic alphabet characters introduce additional challenges which are not present in Latin alphabet characters. In this research we propose a new technique for automatically detecting text in graphics documents, and preparing them for OCR processing. Our detection approach is based on some mathematical measurements to know is it a text or not and to know is it Arabic Based Text or Latin Based. These measurements are follows, measure the Base Line (the line has maximum number of black pixels). Also, measure Item Area (the content of extracted sub images). Finally, find maximum peak for the adjacent black pixels in Base line and maximum length for sub adjacent black pixels. Our experiment results will come in more details. We believe our technique will enable OCR systems to overcome their major shortcoming when dealing with text in graphics images. This will further enable a variety of OCR-based applications to extend their operation to graphics documents such as SPAM detection from image, reading advertisement for blind people, search and index document which contain image, enhancing for printer property (black white or color printer) and enhancing OCR

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

Integrated segmentation and recognition of connected Ottoman script

We propose a novel context-sensitive segmentation and recognition method for connected letters in Ottoman script. This method first extracts a set of segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct sequence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is constructed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman documents reveal that the proposed method provides >90% precision and recall figures in terms of character recognition. In a further set of experiments, we also demonstrate that the framework can be used as a building block for an information retrieval system for digital Ottoman archives. © 2009 Society of Photo-Optical Instrumentation Engineers