Description:FIELD OF THE INVENTION
This invention relates to character recognition of overlapped and touched objects.
BACKGROUND OF THE INVENTION
This project work implements an efficient text segmentation and recognition algorithm for the extraction of text data from printed documents. It involves a system designed to translate images of typewritten text (usually captured by a scanner) into machine editable text. It is a process of classifying optical patterns with respect to alphanumeric or other characters.
http://dx.doi.org/10.1109/IACC.2016.92 disclosed Immense analysis has been done on optical character recognition (OCR). Numerous works has stated for English, Chinese, Devanagari, Malayalam, Arabic scripts, etc. Segmentation has imp phase in OCR and various articles have been published on different segmentation methods like Thinning, histogram etc for different script during last few years. Generally there is not work done on Overlapped and touching scripts. In this article implemented the latest methods of Overlapped character recognition. Segmentation of Overlapped characters has an extremely strenuous task due to the large variety of characters and their shape, font in the script. It is an important step because inaccurate segmentation will cause errors in recognition. Normalization, binarization and thinning are the pre-processing mechanism used in handwritten character recognition. Proposed Method uses threesholding and Blob Analysis for segmentation and to detect overlapped region using Freeman Chain Code. Finally, we used Support Vector Machine (SVM) for resulting feature vectors and obtain classification performance in the character recognition scheme. An overall performance of 93 % at line and curve set of overlapped images are better than existing methods. Here we have empirically performance of segmentation of overlapped characters with the help of different overlapped images.
https://www.iraj.in/journal/journal_file/journal_pdf/3-218-145413382143-46.pdf disclosed Gigantic research has been done on optical character recognition (OCR). Numerous works has stated for English, Chinese and Arabic scripts and Indian languages in [1]. There is no much work done on Overlapped script recognition. Although different efficient methodologies of Character recognition are proposed, but very few research implemented on Overlapped script recognition. In this article implemented the latest methods of Overlapped character recognition. Segmentation of Overlapped characters has an extremely strenuous task due to the large variety of characters and their shape, their shape and intimacy arrival in the script. Normalization, binarization and thinning are the pre-processing techniques used for in handwritten character recognition. Finally, we used Support Vector Machine (SVM) for resulting feature vectors and obtain classification performance in the character recognition scheme in [12]. The implemented recognition scheme provided 93 percent accuracy on Overlapped English character databases respectively
None of the prior art disclosed what the present invention disclosed. Present invention relates to
character recognition of overlapped and touched objects.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This project work implements an efficient text segmentation and recognition algorithm for the extraction of text data from printed documents. It involves a system designed to translate images of typewritten text (usually captured by a scanner) into machine editable text. It is a process of classifying optical patterns with respect to alphanumeric or other characters.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
This application implements an efficient text segmentation and recognition algorithm for the extraction of text data from printed documents. It involves a system designed to translate images of typewritten text (usually captured by a scanner) into machine editable text. It is a process of classifying optical patterns with respect to alphanumeric or other characters.
This application relates to an attempt which is made to recognize characters from English alphabets and numbers by first segmenting them using bounding box and then recognizing each character individually. Each character data set contains 26 alphabets. For connected characters we have used morphological operations (the morphological operations include at least one of dilation, erosion, opening, or closing) that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. For the segmentation of cursive characters watershed algorithm can be used. For the purpose of character recognition template matching technique is used.
• The proposed character recognition system can be further extended to recognize small letters, numerals and text in other languages with different font styles and different font sizes.
• The suggested character recognition system could be further used for pattern recognition as well as voice recognition.
, Claims:1. A method for recognizing characters in an image containing potentially overlapped and touched objects, the method comprising the steps of:
a) processing an input image to facilitate segmentation of individual characters by:
i) applying morphological operations, including at least one of dilation, erosion, opening, or closing, using a structuring element to address connected characters; and
ii) applying a watershed algorithm to address cursive characters;
b) segmenting characters in the processed input image by generating bounding boxes around each individual character; and
c) recognizing each segmented character individually by normalizing its size and orientation and then comparing it to a dataset of character templates using a template matching technique, wherein the character dataset contains representations of the 26 English alphabets and numbers, and wherein the input image is obtained from a scanned printed document.
2. A system for recognizing characters in an image containing potentially overlapped and touched objects, the system configured to perform the steps of the method of claim 1.
3. The system of claim 2, wherein the system further comprises a scanner for capturing the input image.