Text Line Detection in Multicolumn for Indian Scripts Using Histogram: A Document Image Analysis Application

Extraction of text lines from handwritten/printed document images is one of the important steps in the process of an Optical Character Recognition (OCR) system. In case of handwritten document images, presence of skewed, touching or overlapping text line(s) makes this process a real challenge to the researcher. In the present work, a new text line extraction technique based on line contour estimation is reported. Here, digitized document image is initially partitioned into a number of vertical fragments of equal width. Then all the line segments present in these vertical fragments are detected. Finally, the neighboring line segments are analyzed to place them inside the line boundary in which they actually belong. For experimental purpose, the developed technique is tested on CMATERdb1.2.1 database and present technique extracts 88.44% text lines successfully.

Text line extraction from complex handwritten documents, especially for historical collections, is still an unsolved problem. There is a strong demand for reliable and robust approaches since text line extraction is a crucial pre-processing step for modern text recognition and keyword spotting systems. We propose a binarization-free system which employs a newly developed clustering approach based on so-called 'superpixels'. Although multiple ways of generating superpixels were developed in the past, we demonstrate that even a standard method yields impressive results. Our clustering approach is applicable to various scenarios by making use of general characteristics of text lines (e.g., curvilinearity, interline spacings, local homogeneity), and without adapting its parametrization. State-of-the-art results are achieved by the same parametrization for 8 different well-established benchmarking datasets. These datasets cover historical and modern texts as well as images with diverse resolutions and fonts. The system is developed for detecting text lines in complex scenarios. It is not tuned to assign foreground pixels to detected text lines. Thus, superior performance is achieved for the historical datasets for which no pixel hit accuracy of 95% is required. Remarkably, for the dataset of the ICDAR 2015 Competition on Text Line Detection in Historical Documents, the average cost per text line was reduced from 9.77 (winning team) to 8.19.

We examine the use of character image analysis coupled with contextual information in complex data gathering forms to identify and correct optical character recognition (OCR) system rejection and substitution errors. Segmented characters from a complex data gathering form are initially classified using an OCR engine based on a combination of Karhunen-Loeve transforms and a back-propagation neural network. Systems of equations are derived from the data gathering form to determine the values of characters rejected by the OCR engine and to verify the consistency of the data captured. If the OCR results for a single form are determined to be inconsistent with respect to the form's data relationships, a set of decision algorithms which incorporates a second neural network and uses additional character features is used to tag characters according to their likelihood of substitution error. Potential substitution errors are incrementally added to the set of OCR reject errors and are processed through dynamically selected systems of equations and search techniques which correct both error classes. We provide experimental results and determine the extent to which errors can be detected and corrected for various OCR error rates.

Optical Character Recognition (OCR) is a major computer vision task by which characters of image are detected and recognized by comparing to training set images. Process of detecting character is one of the perplexing tasks in computer vision. This is because of input image often not correctly aligned or because of noise. This paper presents a complete Optical Character Recognition (OCR) system which is worked for English character mostly for Calibri font. This system first corrects skew of image if input image is not correctly aligned followed by noise reduction from input image. This process is passed through line and character segmentation that are passed into the recognition module and recognize characters. By experimenting with a set of 50 images, average achievement is 92%, 98% is for Calibri font. Moreover, the developed technique is computationally efficient and requires less time than other Optical character recognition system.

Text line extraction is one of the critical steps in document analysis and optical character recognition (OCR) systems. The purpose of this study is to address the problem of text line extraction of ancient Thai manuscripts written on palm leaves, using an Adaptive Partial Projection (APP) technique by integrating a modified partial projection and smooth histogram with recursion. The proposed approach was compared with a Modified Partial Projection (MPP) looking at vowel analysis and touching components of two consecutive lines. The results from this research suggested that the proposed approach for practical data on palm leaf manuscripts has better performance in solving the line segmentation problem.

Newspapers contain artistic components like headings, advertisements and different stylish posters, which are printed in such a way that text lines are oriented in differently and contain multi-font curved images. WordArt in Microsoft office is one such widely used tools for text multi-orientation, which can also stretch, skew, bend or modify the shape of the text. This type of text is not horizontal and stylized through multi-font properties which makes it difficult to recognize through conventional optical character recognition (OCR) systems. In this paper, a novel technique is proposed which transforms multi-line and multi-font curved text images into single font horizontal format. This method uses vertical and horizontal projection bar threshold techniques to divide text through line segmentation and word segmentation. Each character is then individually reshaped through its centroid in order to align it with the vertical axis. This approach yields high recognition accuracy for text strings which OCR fails to recognize before alignment.KeywordsVertical projectionHorizontal projectionSegmentationCentroidTranslationRotation

Optical Character Recognition (OCR) deals with automated recognition of characters that are in the format of digital image. OCR refers to the process by which scanned images are electronically processed and converted to an editable document. Handwritten and printed texts are the primary research areas of an OCR. Many OCR systems are commercially available for English and Arabic characters but there is still no recognition system available which yields higher recognition rate even though the scanned images are of high quality. The general framework of a Tamil OCR in the literature involves: preprocessing, line segmentation, word segmentation, character segmentation, feature extraction and recognition of characters. OCR for printed Tamil documents poses challenge owing to: one line may have different font styles, presence of pictures, multi columns, touching of adjacent characters, presence of broken characters, low print quality and complex layout. Furthermore, when comparing 26 alphabets in English, Tamil language has 247 alphabets which makes the recognition more difficult. There are few OCRs for Tamil language that are freely available with a moderate recognition rate as the performance comparisons of such OCRs are not available on a benchmark dataset. In this paper we compare OCRs for printed Tamil texts on four different types of documents: books, magazines, newspapers and pamphlets. Furthermore we propose a post-processing error correction technique to the tested OCRs which reduces the overall mean error rate by nearly 10% on those four categories.

Segmentation of text lines is one of the important steps in the Optical Character Recognition system. Text Line Segmentation is pre-processing step of word and character segmentation. Text Line Segmentation can be viewed simple for printing documents which contains distinct spaces between the lines. And it is more complex for the documents where text lines are overlap, touch, curvilinear and variation of space between text lines like in Telugu scripts and skewed documents. The main objective of this project is to investigate different text line segmentation algorithms like Projection Profiles, Run length smearing and Adaptive Run length smearing on low quality documents. These methods are experimented and compare their accuracy and results.

In recent years, automatic document and text analysis has gained significant importance, driven by advancements in optical character recognition (OCR) technology and the need for efficient processing of large volumes of printed or handwritten documents. This article specifically focuses on document layout analysis (DLA) and text line detection (TLD), both of which are crucial components of OCR systems. Our objective is to develop an effective method for extracting both textual and non‐textual regions, addressing challenges unique to the Persian (and Persian‐like) language(s). In the DLA stage, we employ deep learning models and a voting system to accurately determine the regions of interest. Additionally, we introduce methods such as optimum font size concepts, angle correction, and a line curvature elimination algorithm in the TLD process to enhance OCR accuracy. Comparative evaluations against state‐of‐the‐art methods demonstrate the superiority of our approach, showcasing a 2.8% improvement in the accuracy of Tesseract‐OCR 5.1.0 (a well‐established commercial OCR system) on the official Iranian newspapers dataset. These findings underscore the importance of addressing DLA and TLD challenges to advance OCR technology for Persian language documents and provide a solid foundation for future research in this domain.

This paper proposes a character segmentation and retrieval method for a learning support system that analyzes digitized Japanese historical woodblock printed books. The proposed system detects text lines, segments characters, and retrieves similar characters from document images. The process includes background separation, text line extraction, rule-based character integration and segmentation, and similar character retrieval. The experimental results show that the proposed method segmented all text lines correctly and successfully extracted more than 79% of the complicated characters, as well as provided promising character retrieval results.

The text in stylistic documents may have different orientations; the text lines may be curved in shape and they also may not be parallel to each other within a page. As a result, extraction and subsequent recognition of individual text lines and words in such documents is a difficult task. Thinning is one of the most crucial phases in the process of text recognition of characters to a single pixel notation and its success lies in its property to retain the original character shape. Thinning algorithms pose problems due to presence of distinct non-isolated boundaries and complex character shapes in different scripts and produce unwanted edges. This paper presents an improved thinning algorithm which does not produce unwanted edges to get the path of the text for the development of curved straightening system of Optical Character Recognition (OCR). When experimented on documents with either English or Hindi curved text, visual inspection of the results show that proposed method yields promising results.

Character recognition systems have contributed tremendously to the automation of data entry process. Many systems are present in software for character recognition. Digital signal processing has traditionally been done using enhanced microprocessors. While the high volume of generic product provides a low cost solution, its performance fall seriously short for many applications. Until recently, the only alternatives were to develop custom hardware (typically board level or ASIC), buy expensive fixed function processors (e.g. an FFT chip), or use an array of microprocessors. FPGAs offer an opportunity to accelerate your digital signal processing application up to 1000 times over a traditional DSP microprocessor. The main goal here is designing an optical character recognition (OCR) system in FPGA and to develop algorithms which are suitable for hardware implementation of OCR. The application proposed here is automatic calibration of measuring instrument based on OCR system in FPGA. This solution benefits from significant reducing of work quantity for calibration as well as from better replication and objectivity of measuring. The system entirely eliminates influence of the operator on the uncertainty calibration. As system is using FPGA there are many issues to be overcome like speed of operation, programmability and power consumption.

To date, most optical character recognition (OCR) systems process binary document images, and the quality of the input image strongly affects their performance. Since a binarization process is inherently lossy, different algorithms typically produce different binary images from the same gray scale image. The objective of this research is to study effects of global binarization algorithms on the performance of OCR systems. Several binarization methods were examined: the best fixed threshold value for the data set, the ideal histogram method, and Otsu's algorithm. Four contemporary OCR systems and 50 hard copy pages containing 91,649 characters were used in the experiments. These pages were digitized at 300 dpi and 8 bits/pixel, and 36 different threshold values (ranging from 59 to 199 in increments of 4) were used. The resulting 1,800 binary images were processed by all four OCR systems. All systems made approximately 40% more errors from images generated by Otsu's method than those of the ideal histogram method. Two of the systems made approximately the same number of errors from images generated by the best fixed threshold value and Otsu's method.

Optical character recognition (OCR) systems have been widely used to convert documents into digital form. There are lots of both commercial and open source OCR systems available, but a benchmark on Turkish OCR is nonexistent. In this work, we first prepared two publicly available datasets for Turkish OCR, consisting of scanned document images and mobile camera captured document images. Then, we evaluated the Turkish OCR performance of three popular open source OCR systems (Tesseract, CuneiForm, GOCR) on the datasets. Tesseract outperformed the other two on both datasets.

In this paper we present an end-to-end trainable optical character recognition (OCR) system for recognizing machine-printed text in Thai documents. The end-to-end OCR system is based on a script-independent methodology using hidden Markov models. Our system provides an integrated workflow beginning with annotation and transcription of training images to performing OCR on new images with models trained on transcribed training images. The efficacy of our end-to-end OCR system is demonstrated by rapidly configuring our OCR engine for the Thai script. We present experimental results on Thai documents to highlight the specific challenges posed by the Thai script and analyze the recognition performance as a function of amount of training data.