1. A system for handwriting detection and classification in Hindi language comprising: an image input device configured to receive an image of handwritten Hindi text; a pre-processing module configured to pre-process the image to remove noise and enhance the contrast; a feature extraction module configured to extract features from the pre-processed image; a trained classifier module configured to receive the extracted features and classify the handwritten Hindi text into predefined categories; and a display device configured to display the classification result.

2. The system of claim 1, wherein the image input device comprises a scanner, a camera, or any other suitable image acquisition device.

3. The system of claim 1, wherein the pre-processing module comprises noise reduction and contrast enhancement algorithms.

4. The system of claim 1, wherein the feature extraction module comprises histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

5. The system of claim 1, wherein the trained classifier module comprises a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.

6. A method for handwriting detection and classification in Hindi language comprising: receiving an image of handwritten Hindi text; pre-processing the image to remove noise and enhance the contrast; extracting features from the pre-processed image; classifying the handwritten Hindi text into predefined categories using a trained classifier; displaying the classification result.

7. The method of claim 6, wherein the image of handwritten Hindi text is received from a scanner, a camera, or any other suitable image acquisition device.

8. The method of claim 6, wherein pre-processing the image comprises applying noise reduction and contrast enhancement algorithms.

9. The method of claim 6, wherein extracting features from the pre-processed image comprises using histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

10. The method of claim 6, wherein classifying the handwritten Hindi text comprises using a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.   Handwriting detector and classifier for Hindi Abstract A system for handwriting identification and classification in the Hindi language may be included in certain embodiments of the current disclosure. This system may comprise an image input device that is designed to accept an image of handwritten Hindi text. In certain implementations, there is also a pre-processing module that is capable of enhancing the contrast of the picture and removing any noise that may be present in it. A feature extraction module that is set up to extract features from a pre-processed picture may also be included in certain embodiments. A trained classifier module that is designed to receive the extracted characteristics and categorise the handwritten Hindi text into predetermined categories may also be included in embodiments. In certain embodiments, there is additionally a display device that is designed to show the result of the categorization. , Claims:Claims :

1. A system for handwriting detection and classification in Hindi language comprising: an image input device configured to receive an image of handwritten Hindi text; a pre-processing module configured to pre-process the image to remove noise and enhance the contrast; a feature extraction module configured to extract features from the pre-processed image; a trained classifier module configured to receive the extracted features and classify the handwritten Hindi text into predefined categories; and a display device configured to display the classification result.

2. The system of claim 1, wherein the image input device comprises a scanner, a camera, or any other suitable image acquisition device.

3. The system of claim 1, wherein the pre-processing module comprises noise reduction and contrast enhancement algorithms.

4. The system of claim 1, wherein the feature extraction module comprises histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

5. The system of claim 1, wherein the trained classifier module comprises a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.

6. A method for handwriting detection and classification in Hindi language comprising: receiving an image of handwritten Hindi text; pre-processing the image to remove noise and enhance the contrast; extracting features from the pre-processed image; classifying the handwritten Hindi text into predefined categories using a trained classifier; displaying the classification result.

7. The method of claim 6, wherein the image of handwritten Hindi text is received from a scanner, a camera, or any other suitable image acquisition device.

8. The method of claim 6, wherein pre-processing the image comprises applying noise reduction and contrast enhancement algorithms.

9. The method of claim 6, wherein extracting features from the pre-processed image comprises using histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

10. The method of claim 6, wherein classifying the handwritten Hindi text comprises using a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.

Description:Handwriting detector and classifier for Hindi
Field of the Invention
[0001] The present invention relates generally to automatic hand-drawn objects, and more particularly to improved system and method for recognition of characters, such as handwritten Hindi words.

Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Optical character recognition (OCR) is the process of converting scanned images of printed or handwritten text into machine-encoded text. OCR systems are widely used in document processing, including digitization of old books, passport recognition, and license plate recognition. However, the accuracy of OCR systems on handwritten text is lower compared to printed text. Therefore, handwriting detection algorithms are necessary to improve the accuracy of OCR systems on handwritten text. Signature verification is another important application of handwriting detection. Signature verification systems are used to ensure that the signature on a document is genuine and belongs to the intended signer.
[0004] Various technological solutions (e.g., system and method for shape recognition of hand-drawn object, image target identification method and apparatus etc.) for handwriting detection and classification are disclosed in patent literature.
[0005] The JP2005100417A (By- Microsoft Corp) relates to a system and method for shapes recognition of hand-drawn objects. A shape recognizer may recognize a drawing such as a diagram or chart from ink input by recognizing closed containers and/or unclosed connectors in the drawing. The closed containers may represent any number of shapes that may be recognized including circles, ellipses, triangles, quadrilaterals, pentagons, hexagons, and so forth. The unclosed connectors may be any type of connector including lines, curves, arrows, and so forth. Polylines may be used to approximate a skeleton of a connector for handling continuation strokes, overlapping strokes and over-tracing strokes of the skeleton. By using the present invention, a user may draw diagrams and flow charts freely and without restrictions on the hand-drawn input.
[0006] The KR100707195B1 relates to a classifier detection method and apparatus having facial texture information and a face recognition method and apparatus using statistical characteristics of texture information. The classifier detection method having texture information of a face includes detecting a first image and a second image of the same face, dividing the detected first image and the second image into a predetermined size, respectively, and partial images of the divided first image. Detecting first partial images corresponding to the first partial images and second partial images corresponding to the divided second images, and first textures corresponding to texture information of each of the detected first partial images. Generating second texture information corresponding to texture information of each of the pieces of information and the detected second partial images, and comparing the similarity of each of the first texture information with the second texture information corresponding to the first texture information. Detecting, from the first partial images, first classifiers capable of recognizing the identity of the face, in accordance with the inspecting and the similarity inspected. It shall be. Therefore, according to the present invention, in face recognition, since face identity is determined using texture information of a face, face recognition can be accurately performed.
[0007] The WO2019000653A1 relates to an image target identification method and apparatus. The image target identification method includes the steps of: S1, conducting binary processing for each pixel point in an image, and dividing the pixel points into effective pixel points and background points; S2, setting the magnitude of a third threshold according to the total number of pixel points of the image and the size scope of a target to be identified, comparing the number of effective pixel points in the connected area in a binary picture with the third threshold, setting the pixel points in the area as background points if the number is smaller than the third threshold so as to remove the area; S3, determining an external connecting rectangle frame of the remaining connected areas to form a framing area; and S4, taking connected areas with overlapping framing area as a combined integral area, and determining an external connecting rectangle frame of the integral area. In the image, the image content in the external connecting rectangle frame is identified as a target. The target identification method can effectively identify each target objects in the image with low contrast degree.
[0008] However, known technology is not efficient for Hindi based hand written character recognition. Thus, there is need of new technological solution.

Summary
[0009] The present invention relates generally to automatic hand-drawn objects, and more particularly to improved system and method for recognition of characters, such as handwritten Hindi words.
[00010] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00011] The following paragraphs provide additional support for the claims of the subject application.
[00012] Embodiments of the present disclosure may include a system for handwriting detection and classification in Hindi language, wherein the system includes an image input device configured to receive an image of handwritten Hindi text. Embodiments may also include a pre-processing module configured to pre-process the image to remove noise and enhance the contrast. Embodiments may also include a feature extraction module configured to extract features from the pre-processed image. Embodiments may also include a trained classifier module configured to receive the extracted features and classify the handwritten Hindi text into predefined categories. Embodiments may also include a display device configured to display the classification result.
[00013] In some embodiments, the image input device may include a scanner, a camera, or any other suitable image acquisition device. In some embodiments, the pre-processing module may include noise reduction and contrast enhancement algorithms. In some embodiments, the feature extraction module may include histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm. In some embodiments, the trained classifier module may include a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.
[00014] Embodiments of the present disclosure may also include a method for handwriting detection and classification in Hindi language including receiving an image of handwritten Hindi text. Embodiments may also include pre-processing the image to remove noise and enhance the contrast. Embodiments may also include extracting features from the pre-processed image. Embodiments may also include classifying the handwritten Hindi text into predefined categories using a trained classifier. Embodiments may also include displaying the classification result.
[00015] In some embodiments, the image of handwritten Hindi text may be received from a scanner, a camera, or any other suitable image acquisition device. Embodiments may also include pre-processing the image may include applying noise reduction and contrast enhancement algorithms. Embodiments may also include extracting features from the pre-processed image may include using histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm. Embodiments may also include classifying the handwritten Hindi text may include using a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.
Brief Description of the Drawings
[00016] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00017] FIG. 1 is a block diagram illustrating a system for handwriting detection and classification in Hindi language, according to some embodiments of the present disclosure.
[00018] FIG. 2 is a detailed block diagram further illustrating the system (from FIG. 1) for handwriting detection and classification in Hindi language, according to some embodiments of the present disclosure.
[00019] FIG. 3 is a modified block diagram further illustrating the system (from FIG. 1) for handwriting detection and classification in Hindi language, according to some embodiments of the present disclosure.
[00020] FIG. 4 is a flowchart illustrating a method for handwriting detection and classification in Hindi language, according to some embodiments of the present disclosure.
Detailed Description
[00021] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00022] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00023] The present invention relates generally to automatic hand-drawn objects, and more particularly to improved system and method for recognition of characters, such as handwritten Hindi words.
[00024] FIG. 1 is a block diagram that describes a system 100, according to some embodiments of the present disclosure. The system 100 may include an image input device 110 configured to receive an image of handwritten Hindi text, a pre-processing module 120 configured to pre-process the image to remove noise and enhance the contrast, a feature extraction module 130 configured to extract features from the pre-processed image, a trained classifier module 140 configured to receive the extracted features and classify the handwritten Hindi text into predefined categories, and a display device 150 configured to display the classification result. In some embodiments, the feature extraction module 130 may also include histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm. The trained classifier module 140 may include a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.
[00025] FIG. 2 is a block diagram that further describes the system 100 from FIG. 1, according to some embodiments of the present disclosure. The image input device 110 may include a scanner 212, a camera 214, and any other suitable image acquisition device 216.
[00026] FIG. 3 is a block diagram that further describes the system 100 from FIG. 1, according to some embodiments of the present disclosure. The pre-processing module 120 may include noise reduction 322 and contrast enhancement algorithms 324. FIG. 4 is a flowchart that describes a method, according to some embodiments of the present disclosure. At 410, the method may include receiving an image of handwritten Hindi text. At 420, the method may include extracting features from the pre-processed image. At 430, the method may include classifying the handwritten Hindi text into predefined categories using a trained classifier. At 440, the method may include displaying the classification result. Pre-processing the image to remove noise and enhance the contrast.
[00027] The image of handwritten Hindi text may be received from a scanner, a camera, or any other suitable image acquisition device. In some embodiments, Pre-processing the image comprises applying noise reduction and contrast enhancement algorithms, extracting features from the pre-processed image comprises using histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm. In some embodiments, classifying the handwritten Hindi text comprises using a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.
[00028] A system for handwriting recognition and classification in the Hindi language may be included in certain embodiments of the current disclosure. This system may comprise an image input device that is designed to accept an image of handwritten Hindi text. In certain implementations, there is also a pre-processing module that is capable of enhancing the contrast of the picture and removing any noise that may be present in it. A feature extraction module that is set up to extract features from a pre-processed picture may also be included in certain embodiments. A trained classifier module that is designed to receive the extracted characteristics and categorise the handwritten Hindi text into predetermined categories may also be included in embodiments. In certain embodiments, there is additionally a display device that is designed to show the result of the categorization.
[00029] In some implementations, the image input device may take the form of a scanner, a camera, or any other image acquisition tool that is deemed appropriate. In certain implementations, the pre-processing module may have algorithms for the removal and augmentation of noise and contrast, respectively. Histogram of oriented gradients (HOG), local binary patterns (LBP), or any other acceptable feature extraction technique may be included in the feature extraction module of certain implementations. In some implementations, the trained classifier module may include a machine learning model that was educated using a dataset including handwritten Hindi text samples and the categories that correspond to those examples.
[00030] A method for handwriting identification and classification in the Hindi language may also be included in embodiments of the present disclosure. This method may involve the step of receiving an image of handwritten Hindi text. A pre-processing step might also be included in embodiments, with the goal of reducing noise and improving contrast. In certain embodiments, there is additionally the step of extracting features from the picture that has already been pre-processed. In other embodiments, the handwritten Hindi text may also be categorised using a classifier that has been given training based on a set of predetermined criteria. Displaying the categorization result is another option that may be included in embodiments.
[00031] In some implementations, the picture of the handwritten Hindi text may be obtained through a scanner, a camera, or any other image acquisition device that is appropriate for the task at hand. Pre-processing the picture may also be included in certain embodiments. This pre-processing may include using algorithms to reduce noise and boost contrast, for example. In certain embodiments, there is also the possibility of extracting features from the pre-processed picture. This might include making use of a histogram of oriented gradients (HOG), a local binary pattern (LBP), or any other feature extraction approach that is deemed appropriate. In certain embodiments, categorising handwritten Hindi text may also include utilising a machine learning model that has been trained on a dataset that contains examples of handwritten Hindi text together with the categories that correspond to them.
[00032] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00033] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00034] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00035] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00036] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

 

Claims
I/We Claim:
1. A system for handwriting detection and classification in Hindi language comprising:
an image input device configured to receive an image of handwritten Hindi text;
a pre-processing module configured to pre-process the image to remove noise and enhance the contrast;
a feature extraction module configured to extract features from the pre-processed image;
a trained classifier module configured to receive the extracted features and classify the handwritten Hindi text into predefined categories; and
a display device configured to display the classification result.

2. The system of claim 1, wherein the image input device comprises a scanner, a camera, or any other suitable image acquisition device.

3. The system of claim 1, wherein the pre-processing module comprises noise reduction and contrast enhancement algorithms.

4. The system of claim 1, wherein the feature extraction module comprises histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

5. The system of claim 1, wherein the trained classifier module comprises a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.

6. A method for handwriting detection and classification in Hindi language comprising:
receiving an image of handwritten Hindi text;
pre-processing the image to remove noise and enhance the contrast;
extracting features from the pre-processed image;
classifying the handwritten Hindi text into predefined categories using a trained classifier;
displaying the classification result.

7. The method of claim 6, wherein the image of handwritten Hindi text is received from a scanner, a camera, or any other suitable image acquisition device.

8. The method of claim 6, wherein pre-processing the image comprises applying noise reduction and contrast enhancement algorithms.

9. The method of claim 6, wherein extracting features from the pre-processed image comprises using histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

10. The method of claim 6, wherein classifying the handwritten Hindi text comprises using a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.
 

Handwriting detector and classifier for Hindi
Abstract
A system for handwriting identification and classification in the Hindi language may be included in certain embodiments of the current disclosure. This system may comprise an image input device that is designed to accept an image of handwritten Hindi text. In certain implementations, there is also a pre-processing module that is capable of enhancing the contrast of the picture and removing any noise that may be present in it. A feature extraction module that is set up to extract features from a pre-processed picture may also be included in certain embodiments. A trained classifier module that is designed to receive the extracted characteristics and categorise the handwritten Hindi text into predetermined categories may also be included in embodiments. In certain embodiments, there is additionally a display device that is designed to show the result of the categorization. , Claims:Claims
I/We Claim:
1. A system for handwriting detection and classification in Hindi language comprising:
an image input device configured to receive an image of handwritten Hindi text;
a pre-processing module configured to pre-process the image to remove noise and enhance the contrast;
a feature extraction module configured to extract features from the pre-processed image;
a trained classifier module configured to receive the extracted features and classify the handwritten Hindi text into predefined categories; and
a display device configured to display the classification result.

2. The system of claim 1, wherein the image input device comprises a scanner, a camera, or any other suitable image acquisition device.

3. The system of claim 1, wherein the pre-processing module comprises noise reduction and contrast enhancement algorithms.

4. The system of claim 1, wherein the feature extraction module comprises histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

5. The system of claim 1, wherein the trained classifier module comprises a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.

6. A method for handwriting detection and classification in Hindi language comprising:
receiving an image of handwritten Hindi text;
pre-processing the image to remove noise and enhance the contrast;
extracting features from the pre-processed image;
classifying the handwritten Hindi text into predefined categories using a trained classifier;
displaying the classification result.

7. The method of claim 6, wherein the image of handwritten Hindi text is received from a scanner, a camera, or any other suitable image acquisition device.

8. The method of claim 6, wherein pre-processing the image comprises applying noise reduction and contrast enhancement algorithms.

9. The method of claim 6, wherein extracting features from the pre-processed image comprises using histogram of oriented gradients (HOG), local binary patterns (LBP), or any other suitable feature extraction algorithm.

10. The method of claim 6, wherein classifying the handwritten Hindi text comprises using a machine learning model trained on a dataset of handwritten Hindi text samples and their corresponding categories.