Automatic recognition of printed Oriya script

Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation.KeywordsSegmentationOCRPattern recognitionLine segmentationWord segmentationCharacter segmentationTouching lines segmentationOverlapping lines segmentation

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

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.

Performance improvement of character recognition in industrial applications using prior knowledge for more reliable segmentation

Segmentation of line, word and character are one of the critical phases of optical character recognition (OCR). Due to the imperfection in segmentation, most of the recognition system produce poor recognition rate. In this paper we are discussing some novel approach for line, word and character segmentation of printed Manipuri document. Few works has been done for optical character recognition on other Indian script however in case of Manipuri language it is almost negligible. To the best of our knowledge this is the first report on segmentation of documents containing Manipuri script forms. So keeping these things in mind here, in this paper we are discussing some approach to succeed in the above mentioned task. Here first we are discussing about the structure of Manipuri language, and then we discuss some idea for segmentation of line, word and character from degraded Manipuri document. Finally we discuss about various existing recognition technique.

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.

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.

Handwriting analysis has been a subject of research study for several decades. It is a multi stage procedure. In our work, it begins with collecting the handwriting samples on plain white A4 size paper. Preprocessing steps such as binarization and noise removal etc are performed for better recognition. Initially color image or gray scale image is taken as an input then thresholding is done to convert the image into binary image and noise removal technique is also applied. Then line segmentation, word segmentation and character segmentation have been performed. After each segmentation process, normalization techniques have been applied for normalization purpose to find out space between lines, words and letters in handwriting images. Finally, the mean of the space between all the closed loops formed by the characters has been found out and compared with the word spaces to determine the character. This paper focuses on determination of behavior based on space analysis in handwritten document. The proposed method was tested on more than 500 text image of IAM database and sample handwriting images which are written by different writers on different backgrounds, detects the exact space in between lines, words and characters before and after skew normalization of a document. The experimental result shows that proposed algorithm achieves more than 63% accuracy for all type skew angles.

Segmentation is one of the important steps of character recognition system. It is an important step because inaccurate segmentation of characters will cause errors in the recognition stage. In optical character Recognition (OCR) system the presence of touching characters and broken characters decreases the accuracy rate of character recognition. Touching of half character or full character with other full character makes the character segmentation very challenging or difficult task. Segmentation of the broken character is quite difficult because vertical profile projection technique assumes the broken parts of the characters as individual characters. In this paper, we have proposed the method of segmentation for touching and broken characters of handwritten Punjabi text that is the Gurumukhi script. The main purpose of this paper is to provide the new segmentation technique based on neighboring pixels for broken characters and increase the accuracy for touching characters.

In today's world automatic license plate recognition play an important role in monitoring and organizing vehicles. In this paper, we propose a method of detecting and recognizing the license plates of vehicles in an automatic way in our country. The system can be used to collect toll, in car parking and find stolen vehicles. We have used different image processing techniques like resizing image, image binarization, connected component analysis, image enhancement and noise filtering. Our system is composed of four main modules, such as detection of the license plate area, extraction of license plate. Then, segmentation of characters and words and finally recognition of the characters and words. As Bangladesh Road Transport Authority (BRTA) imposed a common standard for vehicle license plate, the size and aspect ratio of all license plates are same. We have used a threshold value to detect and extract the license plate based on our analysis. Afterward, for character segmentation, we used connected components. Later, we used deep learning tool ‘the Convolutional Neural Network’ for character recognition. Due to the lack of a standard data set, we have developed a customized dataset of Bangladeshi number plate for the implementation of our method. The accuracy of our proposed system is 95.42%.

A framework for development of segmentation-free optical recognizers of ancient manuscripts, which work free from line, word, and character segmentation, is proposed. The framework introduces a new representation of visual text using the concept of signature patches. These patches which are free from traditional guidelines of text, such as the baseline, are registered to each other using a microscale registration method based on the estimation of the active regions using a multilevel classifier, the directional map. Then, an one-dimensional feature vector is extracted from the registered signature patches, named spiral features. The incremental learning process is performed using a sparse representation using a dictionary of spiral feature atoms. The framework is applied to the George Washington database with promising results.

There are more than 1000 languages and 14 scripts used by 112 million people in India. All of these scripts divide the document in three parts: Text block, Image block, and Table block. In 21st century, there is a need, obvious reasons, to convert these old printed documents in digital form. Converting them manually is a huge and difficult task. Further it is prone to human errors. Another automated technique is to use Optical character recognition (OCR) system to convert the entire printed document image into editable document. In this paper, an effort has been made to develop OCR technique which converts the printed document into editable document. Firstly a scanned document is preprocessed for noise and skew correction. It is then followed by text-non text classification. Then text line detection has to be performed in the text area. There is no method available which can detect the text line if the image contains the multicolumn text area. In this paper the main contribution is to detect the blocks and detect the text lines in these detected blocks. The technique which can extract the text lines in image document is presented here. After extraction of text lines, word segmentation, character segmentation, and template matching can be performed.

In this paper, we present a novel text line segmentation framework following the divide-and-conquer paradigm: we iteratively identify and re-process regions of ambiguous line segmentation from an input document image until there is no ambiguity. To detect ambiguous line segmentation, we introduce the use of two complimentary line descriptors, referred as to the underline and highlight line descriptors, and identify ambiguities when their patterns mismatch. As a result, we can easily identify already good line segmentations, and largely simplify the original line segmentation problem by only reprocessing ambiguous regions. We evaluate the performance of the proposed line segmentation framework using the ICDAR 2009 handwritten document dataset, and it is close to top-performing systems submitted to the competition. Moreover, the proposed method is also robust against skewness, noise, variable line heights and touching characters. The proposed idea can also be applied to other text analysis tasks such as word segmentation and page layout analysis.

An Adaptive System for Handwriting Recognition