Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Document Image Analysis Techniques for Handwritten Text Segmentation, Document Image Rectification and Digital Collation

Document image analysis comprises all the algorithms and techniques that are utilized to convert an image of a document to a computer readable description. In this work we focus on three such techniques, namely (1) Handwritten text segmentation (2) Document image rectification and (3) Digital Collation. Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different handwriting styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraints on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using a set of lexicons to guide and correct the segmentation and recognition. This approach may fail when the domain lexicon is insufficient. In the first part of this work, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the character size and the number of characters in a word. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the presented method against an existing text segmentation algorithm that uses dynamic programming and achieve significant performance improvement. Digitization of document images using OCR based systems is adversely affected if the image of the document contains distortion (warping). Often, costly and precisely calibrated special hardware such as stereo cameras, laser scanners, etc. are used to infer the 3D model of the distorted image which is used to remove the distortion. Recent methods focus on creating a 3D shape model based on 2D distortion informa- tion obtained from the document image. The performance of these methods is highly dependent on estimating an accurate 2D distortion grid. These methods often affix the 2D distortion grid lines to the text line, and as such, may suffer in the presence of unreliable textual cues due to preprocessing steps such as binarization. In the domain of printed document images, the white space between the text lines carries as much information about the 2D distortion as the text lines themselves. Based on this intuitive idea, in the second part of our work we build a 2D distortion grid from white space lines, which can be used to rectify a printed document image by a dewarping algorithm. We compare our presented method against a state-of-the-art 2D distortion grid construction method and obtain better results. We also present qualitative and quantitative evaluations for the presented method. Collation of texts and images is an indispensable but labor-intensive step in the study of print materials. It is an often used methodology by textual scholars when the manuscript of the text does not exist. Although various methods and machines have been designed to assist in this labor, it still remains an expensive and time- consuming process, often requiring travel to distant repositories for the painstaking visual examination of multiple original copies. Efforts to digitize collation have so far depended on first transcribing the texts to be compared, thus introducing into the process more labor and expense, and also more potential error. Digital collation will instead automate the first stages of collation directly from the document images of the original texts, thereby speeding the process of comparison. We describe such a novel framework for digital collation in the third part of this work and provide qualitative results

Text detection and recognition in images and video sequences

Text characters embedded in images and video sequences represents a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values and complex backgrounds. This thesis investigates methods for building an efficient application system for detecting and recognizing text of any grayscale values embedded in images and video sequences. Both empirical image processing methods and statistical machine learning and modeling approaches are studied in two sub-problems: text detection and text recognition. Applying machine learning methods for text detection encounters difficulties due to character size, grayscale variations and heavy computation cost. To overcome these problems, we propose a two-step localization/verification approach. The first step aims at quickly localizing candidate text lines, enabling the normalization of characters into a unique size. In the verification step, a trained support vector machine or multi-layer perceptrons is applied on background independent features to remove the false alarms. Text recognition, even from the detected text lines, remains a challenging problem due to the variety of fonts, colors, the presence of complex backgrounds and the short length of the text strings. Two schemes are investigated addressing the text recognition problem: bi-modal enhancement scheme and multi-modal segmentation scheme. In the bi-modal scheme, we propose a set of filters to enhance the contrast of black and white characters and produce a better binarization before recognition. For more general cases, the text recognition is addressed by a text segmentation step followed by a traditional optical character recognition (OCR) algorithm within a multi-hypotheses framework. In the segmentation step, we model the distribution of grayscale values of pixels using a Gaussian mixture model or a Markov Random Field. The resulting multiple segmentation hypotheses are post-processed by a connected component analysis and a grayscale consistency constraint algorithm. Finally, they are processed by an OCR software. A selection algorithm based on language modeling and OCR statistics chooses the text result from all the produced text strings. Additionally, methods for using temporal information of video text are investigated. A Monte Carlo video text segmentation method is proposed for adapting the segmentation parameters along temporal text frames. Furthermore, a ROVER (Recognizer Output Voting Error Reduction) algorithm is studied for improving the final recognition text string by voting the characters through temporal frames

EFFICIENT IMAGE COMPRESSION AND DECOMPRESSION ALGORITHMS FOR OCR SYSTEMS

This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the “Nikola Tesla Museum” in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system

Automatic license plate recognition for non commercial vehicles in Ghana to improve road safety

Applied project submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Management Information Systems, April 2016Vehicles are ingenuous contraptions that have significantly revolutionized how human beings move from one place to another. However, inasmuch as they have liberated man, they have also become a major cause of fatalities and injuries. Technologies such as Automatic License Plate Recognition (ALPR) have been utilized in advanced countries to make roads safer to use. However, they are not utilized in lesser developed countries such as Ghana to improve road safety. In this project, an ALPR system is implemented for the Ghanaian context. It is intended to recognize and detect number plates of non-commercial vehicles that jump the red light by using a mobile phone camera and Optical Character Recognition (OCR).Ashesi University Colleg

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%

Text Detection in Natural Scenes and Technical Diagrams with Convolutional Feature Learning and Cascaded Classification

An enormous amount of digital images are being generated and stored every day. Understanding text in these images is an important challenge with large impacts for academic, industrial and domestic applications. Recent studies address the difficulty of separating text targets from noise and background, all of which vary greatly in natural scenes. To tackle this problem, we develop a text detection system to analyze and utilize visual information in a data driven, automatic and intelligent way. The proposed method incorporates features learned from data, including patch-based coarse-to-fine detection (Text-Conv), connected component extraction using region growing, and graph-based word segmentation (Word-Graph). Text-Conv is a sliding window-based detector, with convolution masks learned using the Convolutional k-means algorithm (Coates et. al, 2011). Unlike convolutional neural networks (CNNs), a single vector/layer of convolution mask responses are used to classify patches. An initial coarse detection considers both local and neighboring patch responses, followed by refinement using varying aspect ratios and rotations for a smaller local detection window. Different levels of visual detail from ground truth are utilized in each step, first using constraints on bounding box intersections, and then a combination of bounding box and pixel intersections. Combining masks from different Convolutional k-means initializations, e.g., seeded using random vectors and then support vectors improves performance. The Word-Graph algorithm uses contextual information to improve word segmentation and prune false character detections based on visual features and spatial context. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems, and producing highly accurate text detection masks at the pixel level. To investigate the utility of our feature learning approach for other image types, we perform tests on 8- bit greyscale USPTO patent drawing diagram images. An ensemble of Ada-Boost classifiers with different convolutional features (MetaBoost) is used to classify patches as text or background. The Tesseract OCR system is used to recognize characters in detected labels and enhance performance. With appropriate pre-processing and post-processing, f-measures of 82% for part label location, and 73% for valid part label locations and strings are obtained, which are the best obtained to-date for the USPTO patent diagram data set used in our experiments. To sum up, an intelligent refinement of convolutional k-means-based feature learning and novel automatic classification methods are proposed for text detection, which obtain state-of-the-art results without the need for strong prior knowledge. Different ground truth representations along with features including edges, color, shape and spatial relationships are used coherently to improve accuracy. Different variations of feature learning are explored, e.g. support vector-seeded clustering and MetaBoost, with results suggesting that increased diversity in learned features benefit convolution-based text detectors

Detection and identifitication of registration and fishing gear in vessels

Illegal, unreported and unregulated (IUU) fishing is a global menace to both marine ecosystems and sustainable fisheries. IUU products often come from fisheries lacking conservation and management measures, which allows the violation of bycatch limits or unreported catching. To counteract such issue, some countries adopted vessel monitoring systems (VMS) in order to track and monitor the activities of fishing vessels. The VMS approach is not flawless and as such, there are still known cases of IUU fishing. The present work is integrated in a project PT2020 SeeItAll of the company Xsealence and was included in INOV tasks in which a monitoring system using video cameras in the Ports (Non-boarded System) was developed, in order to detect registrations of vessels. This system registers the time of entry or exit of the vessel in the port. A second system (Boarded System) works with a camera placed in each vessel and an automatic learning algorithm detects and records fishing activities, for a comparison with the vessel’s fishing report.A pesca ilegal, não declarada e não regulamentada (INDNR) é uma ameaça global tanto para os ecossistemas marinhos quanto para a pesca sustentável. Os produtos INDNR são frequentemente provenientes de pescas que não possuem medidas de conservação e de gestão, o que permite a violação dos limites das capturas ou a captura não declarada. Para contrariar esse problema, alguns países adotaram sistemas de monitoramento de embarcações (VMS) para acompanhar e monitorar as atividades dos navios de pesca. A abordagem VMS não é perfeita e, como tal, ainda há casos conhecidos de pesca INDNR. O presente trabalho encontra-se integrado num projeto PT2020 SeeItAll da empresa Xsealence. Este trabalho integrado nas tarefas do INOV no qual foi desenvolvido um sistema de monitorização das entradas dos navios nos Portos (Sistema não embarcado) no qual pretende-se desenvolver um sistema que detete as matriculas dos navios registando a hora de entrada e saída do porto com recurso da camaras de vídeo. A outra componente (sistema embarcado) é colocada em cada embarcação uma camara de video e, recorrendo a aprendizagem automática e um sistema de CCTV, são detetadas as atividades de pesca e gravadas, para posterior comparação com o relatório de pesca do navio