Handwritten Manuscript Digitizer

2021

Handwriting recognition has been one of the most fascinating and challenging research areas in field of image processing and pattern recognition. It contributes enormously to the improvement of automation process. In this paper, a system for recognition of unconstrained handwritten Malayalam characters is proposed. A database of 10,000 character samples of 44 basic Malayalam characters is used in this work. A discriminate feature set of 64 local and 4 global features are used to train and test SVM classifier and achieved 92.24% accuracy.

arXiv (Cornell University), 2012

Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system gives better recognition result due to better discrimination capability of the NN. A major problem in handwriting recognition is the huge variability and distortions of patterns. Elastic models based on local observations and dynamic programming such HMM are not efficient to absorb this variability. But their vision is local. But they cannot face to length variability and they are very sensitive to distortions. Then the SVM is used to estimate global correlations and classify the pattern. Support Vector Machine (SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better recognition result. The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network

The recognition of handwritten characters in general, or (arabic) digits, in a more specific sense, is defined as the ability to interpret the legible handwritten input from sources such as paper documents. One distinguishes between offline -the recognition is done after the handwriting act is complete and the algorithm input is an image or scan of the handwritten characters or digits -and online recognition, where the recognition is done at the time of writing and is most common in pen/stylus-based screen interfaces. In this project, the focus will be on the former type of handwritten digits recognition, namely offline recognition. The main challenges attributed to this problem are linked to the variety of handwriting styles, of transformations each individual applies to the representation of a perfect digit. Once these challenges are overcome, the applications of an algorithm that can correctly classify handwritten digits span a large range of practical domains -from scanning and transforming handwritten postal codes to digitizing handwritten manuscripts or archive documents. This document introduces two supervised learning algorithms used for the classification of handwritten digits and is structured as follows: Section 1 describes the project task, Section 2 presents a high-level overview of the employed methods and of the obtained results, Section 3 provides a deeper description of the methods used, while Section 4 documents the results. Lastly, Section 5 discusses these results and provides an account of future improvements.

2006

A system for recognition of online handwritten characters has been presented for Indian writing systems. A handwritten character is represented as a sequence of strokes whose features are extracted and classified. Support vector machines have been used for constructing the stroke recognition engine. The results have been presented after testing the system on Devanagari and Telugu scripts.

International Journal of Computer …, 2012

Handwriting recognition has attracted many researchers across the world. Recognition of online handwritten Hindi numerals is a goal of many research efforts in the pattern recognition field. This paper presents an online handwritten Hindi numeral recognition system using Support Vector Machines. Co-ordinate points of the input handwritten numeral are collected; various algorithms for pre-processing are applied for normalizing, resampling and interpolating missing points. Angle, curvature along with the x and y coordinates are extracted from the input handwritten numeral. The data obtained is then used for recognition using the kernel functions of SVM. The recognition accuracies are obtained on different schemes of data using the four kernel functions of SVM.

In the present scenario most of the importance is given for the " paperless office " there by more and more communication and storage of documents is performed digitally. Documents and files which are present in Hindi and Marathi languages that were once stored physically on paper are now being converted into electronic form in order to facilitate quicker additions, searches, and modifications, as well as to prolong the life of such records. Because of this, there is a great demand of such software, which automatically extracts, analyze, recognize and store information from physical documents for later retrieval. Skew detection is used for text line position determination in Digitized documents, automated page orientation, and skew angle detection for binary document images, skew detection in handwritten scripts, in compensation for Internet audio applications and in the correction of scanned documents.

IRJET, 2021

Developing an android application for character recognition to read the text from an image is a big area of research. Nowadays, there is a trend of storing information from the handwritten documents for future use. The translated machine encoded text can be easily edited, searched and can be processed in many other ways according to requirements. Character recognition systems translate such scanned images of printed, typewritten or handwritten documents into machine encoded text. The method to transform handwritten data into electronic format is Optical Character Recognition. It involves several steps including pre-processing, segmentation, feature extraction and post-processing. Picture information is improved with the help of a technique named image pre-processing. The main challenge is to recognize the characters from different styles of handwriting. Thus, a system is designed that recognizes the handwritten data to obtain an editable text. The output of this system depends upon the data that has to be written by the writer. The representation of samples as points in space that are mapped such that the samples of individual categories can be differentiated using a major vector is known as SVM model. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition, high accuracy, precision and recall as compared to existing method.

In Sindhi Language, handwritten text feature extraction is such a challenging task for all scholars, because different people write in different styles or manners, to analyze each text is such a complex problem. Feature extraction of text segmentation, classifying each character and labelling for training data to recognize text for different handwritings and testing for analyzing features of providing handwritten text data .In this research, SVM (support vector machine) is used for analyzing and tokenizing each character or word of Sindhi Language text and transform into suitable information with efficiency & accuracy. The research is not only useful for improving the knowledge of Sindhi Handwritten Text Recognition but it can be beneficial for other recognition systems

2016

Robust handwritten Marathi character recognition is essential to the proper function in document analysis field. Many researches in OCR have been dealing with the complex challenges of the high variation in character shape, structure and document noise. In proposed system, noise is removed by using morphological and thresholding operation. Skewed scanned pages and segmented characters are corrected using Hough Transformation. The characters are segmented from scanned pages by using bounding box techniques. Size variation of each handwritten Marathi characters are normalized in 40 \(\times \) 40 pixel size. Here we propose feature extraction from handwritten Marathi characters using connected pixel based features like area, perimeter, eccentricity, orientation and Euler number. The modified k-nearest neighbor (KNN) and SVM algorithm with five fold validation has been used for result preparation. The comparative accuracy of proposed methods are recorded. In this experiment modified SV...

International Journal of Image, Graphics and Signal Processing, 2013

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets.