Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system

Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

A framework for ancient and machine-printed manuscripts categorization

Document image understanding (DIU) has attracted a lot of attention and became an of active fields of research. Although, the ultimate goal of DIU is extracting textual information of a document image, many steps are involved in a such a process such as categorization, segmentation and layout analysis. All of these steps are needed in order to obtain an accurate result from character recognition or word recognition of a document image. One of the important steps in DIU is document image categorization (DIC) that is needed in many situations such as document image written or printed in more than one script, font or language. This step provides useful information for recognition system and helps in reducing its error by allowing to incorporate a category-specific Optical Character Recognition (OCR) system or word recognition (WR) system. This research focuses on the problem of DIC in different categories of scripts, styles and languages and establishes a framework for flexible representation and feature extraction that can be adapted to many DIC problem. The current methods for DIC have many limitations and drawbacks that restrict the practical usage of these methods. We proposed an efficient framework for categorization of document image based on patch representation and Non-negative Matrix Factorization (NMF). This framework is flexible and can be adapted to different categorization problem. Many methods exist for script identification of document image but few of them addressed the problem in handwritten manuscripts and they have many limitations and drawbacks. Therefore, our first goal is to introduce a novel method for script identification of ancient manuscripts. The proposed method is based on patch representation in which the patches are extracted using skeleton map of a document images. This representation overcomes the limitation of the current methods about the fixed level of layout. The proposed feature extraction scheme based on Projective Non-negative Matrix Factorization (PNMF) is robust against noise and handwriting variation and can be used for different scripts. The proposed method has higher performance compared to state of the art methods and can be applied to different levels of layout. The current methods for font (style) identification are mostly proposed to be applied on machine-printed document image and many of them can only be used for a specific level of layout. Therefore, we proposed new method for font and style identification of printed and handwritten manuscripts based on patch representation and Non-negative Matrix Tri-Factorization (NMTF). The images are represented by overlapping patches obtained from the foreground pixels. The position of these patches are set based on skeleton map to reduce the number of patches. Non-Negative Matrix Tri-Factorization is used to learn bases from each fonts (style) and then these bases are used to classify a new image based on minimum representation error. The proposed method can easily be extended to new fonts as the bases for each font are learned separately from the other fonts. This method is tested on two datasets of machine-printed and ancient manuscript and the results confirmed its performance compared to the state of the art methods. Finally, we proposed a novel method for language identification of printed and handwritten manuscripts based on patch representation and Non-negative Matrix Tri-Factorization (NMTF). The current methods for language identification are based on textual data obtained by OCR engine or images data through coding and comparing with textual data. The OCR based method needs lots of processing and the current image based method are not applicable to cursive scripts such as Arabic. In this work we introduced a new method for language identification of machine-printed and handwritten manuscripts based on patch representation and NMTF. The patch representation provides the component of the Arabic script (letters) that can not be extracted simply by segmentation methods. Then NMTF is used for dictionary learning and generating codebooks that will be used to represent document image with a histogram. The proposed method is tested on two datasets of machine-printed and handwritten manuscripts and compared to n-gram features (text-based), texture features and codebook features (imagebased) to validate the performance. The above proposed methods are robust against variation in handwritings, changes in the font (handwriting style) and presence of degradation and are flexible that can be used to various levels of layout (from a textline to paragraph). The methods in this research have been tested on datasets of handwritten and machine-printed manuscripts and compared to state-of-the-art methods. All of the evaluations show the efficiency, robustness and flexibility of the proposed methods for categorization of document image. As mentioned before the proposed strategies provide a framework for efficient and flexible representation and feature extraction for document image categorization. This frame work can be applied to different levels of layout, the information from different levels of layout can be merged and mixed and this framework can be extended to more complex situations and different tasks

Analysis of the changes in the tarcrete layer on the desert surface of Kuwait using satellite imagery and cell-based modeling

Thesis (Ph.D.)--Boston UniversityThe 1991 Gulf War caused massive environmental damage in Kuwait. Deposition of oil and soot droplets from hundreds of burning oil-wells created a layer of tarcrete on the desert surface covering over 900 km'. This research investigates the spatial change in the tarcrete extent from 1991 to 1998 using Landsat Thematic Mapper (TM) imagery and statistical modeling techniques. The pixel structure ofTM data allows the spatial analysis of the change in tarcrete extent to be conducted at the pixel (cell) level within a geographical information system (GIS). There are two components to this research. The first is a comparison of three remote sensing classification techniques used to map the tarcrete layer. The second is a spatial-temporal analysis and simulation of tarcrete changes through time. The analysis focuses on an area of 389 km' located south of the Al-Burgan oil field. Five TM images acquired in 1991, 1993, 1994, 1995, and 1998 were geometrically and atmospherically corrected. These images were classified into six classes: oil lakes; heavy, intermediate, light, and traces of tarcrete; and sand. The classification methods tested were unsupervised, supervised, and neural network supervised (fuzzy ARTMAP). Field data of tarcrete characteristics were collected to support the classification process and to evaluate the classification accuracies. Overall, the neural network method is more accurate (60 percent) than the other two methods; both the unsupervised and the supervised classification accuracy assessments resulted in 46 percent accuracy. The five classifications were used in a lagged autologistic model to analyze the spatial changes of the tarcrete through time. The autologistic model correctly identified overall tarcrete contraction between 1991-1993 and 1995-1998. However, tarcrete contraction between 1993-1994 and 1994-1995 was less well marked, in part because of classification errors in the maps from these time periods. Initial simulations of tarcrete contraction with a cellular automaton model were not very successful. However, more accurate classifications could improve the simulations. This study illustrates how an empirical investigation using satellite images, field data, GIS, and spatial statistics can simulate dynamic land-cover change through the use of a discrete statistical and cellular automaton model

Historical Land use/Land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors: a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects

This thesis deals spatially and regionally with the natural boundaries of the Euphrates River Basin (ERB) in Syria. Scientifically, the research covers the application of remote sensing science (optical remote sensing: LANDSAT-MSS, TM, and ETM+; and TERRA: ASTER); and methodologically, in Land Use/Land Cover (LULC) classification and mapping, automatically and/or semi-automatically; in LULC-change detection; and finally in the mapping of historical irrigation and agricultural projects for the extraction of differing crop types and the estimation of their areas. With regard to time, the work is based on the years 1975, 1987, 2005 and 2007. Initially, preprocessing of the satellite data (geometric- and radiometric- processing, image enhancement, best bands composite selection, transformation, mosaicing and finally subsetting) was carried out. Then, the Land Use/Land Cover Classification System (LCCS) of the Food and Agriculture Organization (FAO) was chosen. The following steps were followed in LULC- classification and change detection mapping: visual interpretation in addition to digital image processing techniques; pixel-based classification methods; unsupervised classification: ISODATA-method; and supervised classification and multistage supervised approaches using the algorithms: Maximum Likelihood Classifier (MLC), Neural Network classifier (NN) and Support Vector Machines (SVM). These were trialed on a test area to determine the optimized classification approach/algorithm for application on the whole study area (ERB) based on the available imagery. Pre- and post- classification change detection methods (comparison approaches) were used to detect changes in land use/land cover-classes (for the years 1975, 1987 and 2007) in the study area. The remote sensing methods show a high potential in mapping historical and present land use/land cover classes and its changes over time. Significant results are also possible for agricultural crop classification in relatively large regional areas (the ERB in Syria is almost 50,335 km²). Change trends in the study area and period was characterized by land-intensive agricultural expansion. The rapid, more labor- and capital- intensive growth in the agricultural sector was enabled by the introduction of fertilizer, improved access to rural roads and markets, and the expansion of the government irrigation projects. Irrigated areas increased 148 % in the past 32 years from 249,681 ha in 1975 to 596,612 ha in 2007