TECHNICAL FIELD
[001] The present disclosure relates to a text input and prediction system. More particularly, the present disclosure related to systems and methods for recognition of handwritten text.
BACKGROUND
[002] 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.
[003] Handwriting is one of the most basic modes of communication between human beings. In the earlier times, handwritten notes were used to keep the record of day-to-day information in various organizations such as schools, banks, government and private offices, police stations, and hospitals etc. With the advent of computers, the input devices, i.e., keyboard, mouse and joystick are used by the human beings for communication with computers, these input devices, however, have certain limitations of capturing input through natural handwriting. With the passage of time, a great revolution has taken place in the Information Technology (IT) sector by the enhancement of advanced human-computer interfacing devices. These days, the two natural ways of communication between human beings and computers are speech (or voice) and handwriting. In the present study, we have focused on one of these ways, namely, handwriting.
[004] Handwriting recognition refers to the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch- screen based mobile devices, digital-pen/stylus-based devices, Tablet-PC, digitizers etc. Handwriting recognition has two flavours, namely, offline Handwriting Recognition and Online Handwriting Recognition. The primary source of input to the computer in offline handwriting recognition are scanned images of handwritten documents. On the other hand, in online handwriting recognition the sources of input are handwriting signals, captured from pen traces on the surface of a digital device. These online signals are normally the pen trajectories, stored as x- and y-coordinates of each captured point. In both cases, the handwriting is analysed, pre-processed, recognized and uniquely mapped to a digital representation of the original handwriting.
- 3 -
[005] Many of the rural areas, most of the conversation is done with the regional language. Moreover, it is mandatory for every State (region) to do the official conversation in their regional scripting language. Due to the great revolution in the IT-Sector, the traditional way of conversation (computer keyboard) system is going to replace with user friendly pen-based natural input interfaces. The dispersion of various human-computer interfacing devices such as smart-phone, Tablet-PC, touch-based computers etc., have necessitated the development of handwriting recognition system for respective regional scripts. Through this study, we aim to build an accurate automatic and suggestion based online handwriting recognition system for Gurmukhi script.
[006] Gurmukhi script is used for writing the Punjabi language. Punjabi language is an Indo-Aryan language with more than 100 million native speakers in the Indian subcontinent and spread with the Punjabi diaspora worldwide. It is the most widely spoken language in the Punjab and other countries worldwide. Apart from India, it has a significant presence in the United Arab Emirates, the United States, the United Kingdom, Australia, New Zealand, Italy, Pakistan and the Netherlands. Even though, the vocabulary is small, the handwriting recognition system can find many practical applications, like census data collection, form filling, personal e-handouts etc. especially in the Indian context.
[007] There is therefore a need in the art to provide a handwritten text recognition system and method that seeks to overcome or at least ameliorate one or more of the abovementioned problems and other limitations of the existing solutions and utilize techniques, which are robust, accurate, fast, efficient, cost-effective and simple.
OBJECTS OF THE PRESENT DISCLOSURE
[008] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[009] It is an object of the present disclosure to provide system and method for recognition of handwritten text.
[0010] It is another object of the present disclosure to provide system and method for the recognition of handwritten textthat enables auto-correction of the recognized text.
[0011] It is another object of the present disclosure to provide system and method for the recognition of handwritten text that enables the prediction of upcoming letters (characters) and words for the recognized text.
[0012] It is another object of the present disclosure to provide system and method for the recognition of handwritten text that enables pronunciation of the recognised word as well as meaning of the recognized text.
- 4 -
[0013] It is another object of the present disclosure to provide system and method for the recognition of handwritten text that is cost effective and easy to implement.
[0014] It is yet another object of the present disclosure to provide system and method for recognition of handwritten text that enhances adaptability to new handwritten texts.
SUMMARY
[0015] The present disclosure relates to a text input and prediction system. More particularly, the present disclosure related to systems and methods for recognition of handwritten text.
[0016] According to an aspect, the present disclosure provides a handwritten text recognition system, the system comprising: an input device to receives a sample of handwritten content as input; one or more processors of a computing device, the one or more processors operatively coupled to the input device; and a memory device coupled with the one or more processors, the memory device storing a set of instructions executable by the one or more processors to: receive the input sample of handwritten content; extract one or more visual features from the received sample of handwritten content to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language; determine one or more letters of the language by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language; and predict, for the determined one or more letters, a following combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters.
[0017] In an embodiment, the input device is selected from a group comprising, a touch pad, a touch-enabled screen of a computing device, an optical sensor, and an image scanner.
[0018] In an embodiment, the language comprises Gurmukhi or Punjabi language.
[0019] In an embodiment, a combination of the one or more letters and the one or more characters determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
- 5 -
[0020] In an embodiment, the system comprising a presentation unit operatively coupled to the one or more processors, the presentation unit configured to present the predicted following combination of one or more letters and one or more characters.
[0021] In an embodiment, presentation by the presentation unit comprises any or a combination of audio pronunciation and visual display of the predicted following combination of one or more letters and one or more characters.
[0022] In an embodiment, the presentation unit configured to present dictionary meaning of the predicted following combination of one or more letters and one or more characters based on a fifth dataset comprising words of the language and the dictionary meaning of the words of the language.
[0023] Another aspect of the present disclosure relates to a method for recognizing handwritten texts, the method comprises: receiving, by an input device, a sample of handwritten content as input; receiving, by one or more processors, the input sample of handwritten content; extracting, by the one or more processors, the one or more visual features from the received sample of handwritten content to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language; determining, by the one or more processors, one or more letters of the language by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language; and predicting, by the one or more processors, for the determined one or more letters, a following combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters.
[0024] In an embodiment, a combination of the one or more letters and the one or more characters are used to determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
[0025] In an embodiment, the method comprising a presentation unit operatively coupled to the one or more processors, the presentation unit configured to present the predicted following combination of one or more letters and one or more characters.
- 6 -
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0027] FIG. 1 illustrates an exemplary network architecture in which or with which proposed system can be implemented in accordance with an embodiment of the present disclosure.
[0028] FIG. 2 illustrates an exemplary module diagram for the recognition of handwritten text in accordance with an embodiment of the present disclosure.
[0029] FIG. 3 is a flow diagram illustrating a process for the recognition of handwritten text in accordance with an embodiment of the present disclosure.
[0030] FIGs. 4A and 4Billustrate exemplary representations of recognition of handwritten text and prediction of possible upcoming characters and words in accordance with an embodiment of the present disclosure.
[0031] FIG. 5 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
DETAILED DESCRIPTION
[0032] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered 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 spirit and scope of the present disclosure as defined by the appended claims.
[0033] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0034] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-
- 7 -
purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and/or by human operators.
[0035] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.
[0036] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0037] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0038] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Various modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). Also, the terminology and phraseology used is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications, and equivalents consistent with the principles and features disclosed. For the purpose of clarity,
- 8 -
details relating to technical material that is known in the technical fields related to the invention have not been described in detail so as not to unnecessarily obscure the present invention.
[0039] Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named element.
[0040] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program the computer (or other electronic devices) to perform a process. The term “machine-readable storage medium” or “computer-readable storage medium” includes, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).A machine-readable medium may include a non-transitory medium in which data may be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data,
- 9 -
arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
[0041] Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a machine-readable medium. A processor(s) may perform the necessary tasks.
[0042] Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system.
[0043] Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases, it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
[0044] All methods described herein may 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 with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0045] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0046] The present disclosure relates to text input and prediction system. More particularly, the present disclosure related to systems and methods for recognition of handwritten text.
[0047] According to an aspect, the present disclosure provides a handwritten text recognition system, the system comprising: an input device to receive a sample of
- 10 -
handwritten content as input; one or more processors of a computing device, the one or more processors operatively coupled to the input device; and a memory device coupled with the one or more processors, the memory device storing a set of instructions executable by the one or more processors to: receive the input sample of handwritten content; extract one or more visual features from the received sample of handwritten content to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language; determine one or more letters of the language by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language; and predict, for the determined one or more letters, a following combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters.
[0048] In an embodiment, the input device is selected from a group comprising, a touch pad, touch-enabled screen of a computing device, an optical sensor, and an image scanner.
[0049] In an embodiment, the language comprises Gurmukhi or Punjabi language.
[0050] In an embodiment, a combination of the one or more letters and the one or more characters determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
[0051] In an embodiment, the system comprising a presentation unit operatively coupled to the one or more processors, the presentation unit configured to present the predicted following combination of one or more letters and one or more characters.
[0052] In an embodiment, presentation by the presentation unit comprises any or a combination of audio pronunciation and visual display of the predicted following combination of one or more letters and one or more characters.
[0053] In an embodiment, the presentation unit configured to present dictionary meaning of the predicted following combination of one or more letters and one or more characters based on a fifth dataset comprising words of the language and the dictionary meaning of the words of the language.
[0054] Another aspect of the present disclosure relates to a method for recognizing hand-written texts, the method comprises: receiving, by an input device, a sample of
- 11 -
handwritten content as input; receiving, by one or more processors, the input sample of handwritten content; extracting, by the one or more processors, the one or more visual features from the received sample of handwritten content to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language; determining, by the one or more processors, one or more letters of the language by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language; and predicting, by the one or more processors, for the determined one or more letters, a following combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters.
[0055] In an embodiment, a combination of the one or more letters and the one or more characters are used to determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
[0056] In an embodiment, the method comprising a presentation unit operatively coupled to the one or more processors, the presentation unit configured to present the predicted following combination of one or more letters and one or more characters.
[0057] FIG. 1 illustrates an exemplary network architecture in which or with which proposed system can be implemented in accordance with an embodiment of the present disclosure.
[0058] As illustrated, in a network implementation 100, the system 102 can be communicatively coupled with a plurality of computing devices 106-1, 106-2…106-N (collectively referred to as computing devices 106 and individually referred to as computing device 106 hereinafter) through network 104. The system 102 can be implemented using any or a combination of hardware components and software components such as a server 112, a computing system, a computing device, a security device and the like.
[0059] Further, the system 102 can interact with input devices 108-1, 108-2…108-N (collectively referred to as input devices 108, and individually referred to as input device 108 hereinafter), through the computing devices 106 or through applications residing on the computing devices 106. In an implementation, the system 102 can be accessed by applications residing on any operating system, including but not limited to, AndroidTM, iOSTM, and the like. Examples of the computing devices 106 can include but are not limited
- 12 -
to, a portable computer, a personal digital assistant, a handheld device, and a workstation. In a preferred embodiment, the computing devices 106 are mobile phones associated with respective input devices 108.
[0060] In an embodiment, the input device 108 can include a touch pad, touch enabled screen of a computing device, an optical sensor, an image scanner and the like that can be used to receive a handwriting input that forms part of an input to the system 102.
[0061] In an embodiment, the input device 108 can be implemented such that it forms part of the computing device 106. For example, the input device 108 can be a touch screen implemented with mobile device 106 to receive a handwritten text from the user.
[0062] In an embodiment, the system 102 can receive the input from the input device 108. Further, the received handwritten text can be received by using a device, apparatus or hand to form one or more letters of a language. In another embodiment, the input device 108 can be an optical sensor that can be used for capturing the image of the handwritten text that can be received by the system for further processing.
[0063] In an embodiment, the system 102 can be configured to extract one or more visual features from the received sample of handwritten content. The extracted one or more features can be used to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language. In an embodiment, the first data asset can be stored in a first database. It would be appreciated that the first database can be present on a cloud/ server.
[0064] In an embodiment, the system 102 can be configured to determine one or more letters of the language. The system 102 can be configured to determine one or more letters by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language. In an embodiment, the second data asset can be stored in a second database. It would be appreciated that the second database can be present on a cloud/ server.
[0065] In an embodiment, the system 102 can be configured to predict one or more letters to form a word of the language. Further, for the determined one or more letters, a combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters. In an embodiment, the third data asset can be stored in a third database. It would be appreciated that the third database can be present on a cloud/ server. In an embodiment, the language can comprise but not limited to Gurmukhi, Punjabi, Hindi and Devanagari.
- 13 -
[0066] It would be appreciated by the person skilled in the art that although the embodiments have been described in terms of Gurmukhi, Punjabi, Hindi and Devanagari. However, the systems and methods can be used for other languages as well.
[0067] In an embodiment, the system 102 can be configured to combine one or more letters and the one or more characters determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
[0068] In an embodiment, the system can comprise a presentation unit (not shown). The presentation unit can be configured to either visually display the predicted words or can be configured to pronounce the predicted words. In an embodiment, the system 102 can include a fifth dataset can comprise words of the language and the dictionary meaning of the words of the language. In an embodiment, the fifth dataset can further comprise corresponding audio file or visual presentation of the corresponding predicted word and the dictionary meaning.
[0069] For example, the system 102 receives a handwritten text from the input device 108 and the system 102 recognizes the word and can provide dictionary meaning of the word as well as provide the pronunciation of the word as well as the dictionary meaning of the word.
[0070] FIG. 2 illustrates an exemplary module diagram for recognition of handwritten text in accordance with an embodiment of the present disclosure.
[0071] In an aspect, module diagram 200 of the system 102 may comprise one or more processor(s) 202. The one or more processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in a memory 206 of the system 102. The memory 206 may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 206 may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0072] The system 102 may also comprise an interface(s) 204. The interface(s) 204 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 204 may
- 14 -
facilitate communication of system 102. The interface(s) 204 may also provide a communication pathway for one or more components of the system 102. Examples of such components include, but are not limited to, processing engine(s) 208 and data 210.
[0073] The processing engine(s) 208 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 208. In such examples, the system 102 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to system 102 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry.
[0074] The data 210 may comprise data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208 or the system 102.
[0075] In an exemplary embodiment, the processing engine(s) 208 may include a sample text receive engine 212, a visual feature extraction engine 214, a text determination engine 216, a text prediction engine 218, a presentation engine 220 and another engine (s) 222.
[0076] In an embodiment, the sample text receives engine 212 can be configured to receive the input from the input device 108. Further, the received handwritten text can be received by using a device, apparatus or hand to form one or more letters of a language. In another embodiment, the input device 108 can be an optical sensor that can be used for capturing the image of the handwritten text that can be received by the system for further processing.
[0077] In an embodiment, the visual feature extraction engine 214 can be configured to extract one or more visual features from the received sample of handwritten content. The extracted one or more features can be used to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language. In an
- 15 -
embodiment, the first data asset can be stored in a first database. It would be appreciated that the first database can be present on a cloud/ server.
[0078] In an embodiment, the text determination engine 216 can be configured to predict one or more letters to form a word of the language. Further, for the determined one or more letters, a combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters. In an embodiment, the third data asset can be stored in a third database. It would be appreciated that the third database can be present on a cloud/ server. In an embodiment, the language can comprise but not limited to Gurmukhi, Punjabi, Hindi, Devanagari.
[0079] In an embodiment, the presentation engine 220 can be configured to present the predicted combination of letters or words using a presentation unit. The presentation unit can be configured to either visually display the predicted words or can be configured to pronounce the predicted words. In an embodiment, the system 102 can include a fifth dataset can comprise words of the language and the dictionary meaning of the words of the language. In an embodiment, the fifth dataset can further comprise a corresponding audio file or visual presentation of the corresponding predicted word and the dictionary meaning.
[0080] FIG. 3 is a flow diagram illustrating a process for recognition of handwritten text in accordance with an embodiment of the present disclosure.
[0081] In an aspect, the proposed method may be described in the general context of computer-executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method can also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer-executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0082] The order in which the method as described is not intended to be construed as a limitation and any number of the described method blocks may be combined in any order to implement the method or alternate methods. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above-described system.
- 16 -
[0083] In the context of flow diagram 300, at block 302, an input device operatively coupled with a computing device can assist in receiving a sample of handwritten content as input. Further block 304 pertains to receiving the input sample of handwritten content by using one or more processors of the computing device. further, block 306 pertains to extracting the one or more visual features from the received sample of handwritten content to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language.
[0084] Further, block 308 pertains to determining one or more letters of the language by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language. further, step 310 pertains to predicting, the determined one or more letters, a following combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters.
[0085] Further, combination of the one or more letters and the one or more characters are used to determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
[0086] FIGs. 4A and 4B illustrate exemplary representations of recognition of handwritten text and prediction of possible upcoming characters and words in accordance with an embodiment of the present disclosure.
[0087] In an exemplary embodiment, as illustrates, input can be received at an input block 402. The input block 402 can be configured such that a handwritten text sample can be received. Further, block 404 can be configured to present the determined text based on the extraction of one or more visual features from the received sample of handwritten content to determine one or more characters. Further, block 406 can be used for providing a prediction of texts based on pre-stored dataset based on historical occurrence of the character or words or combination of words. Further, block 408 can be configured to display the final predicted text.
[0088] FIG. 5 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
- 17 -
[0089] As shown in FIG. 5, computer system 500 can include an external storage device 510, a bus 520, a main memory 530, a read only memory 540, a mass storage device 550, communication port 560, and a processor 570. A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Examples of processor 570 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on chip processors or other future processors. Processor 570 may include various modules associated with embodiments of the present invention. Communication port 560 can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port 560 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0090] Memory 530 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory 540 can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 570. Mass storage 550 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0091] Bus 520 communicatively couples processor(s) 570 with the other memory, storage and communication blocks. Bus 520 can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor 570 to software system.
[0092] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 520 to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 560. The
- 18 -
external storage device 510 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), Digital Video Disk-Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0093] Embodiments of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or combining software and hardware implementation that may all generally be referred to herein as a “circuit,” “module,” “component,” or “system.” Furthermore, aspects of the present disclosure may take the form of a computer program product comprising one or more computer readable media having computer readable program code embodied thereon.
[0094] Thus, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named.
[0095] As used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean “communicatively coupled with” over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.
[0096] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all
- 19 -
terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0097] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
ADVANTAGES OF THE PRESENT DISCLOSURE
[0098] The present disclosure provides a system and method for the recognition of handwritten text.
[0099] The present disclosure provides a system and method for the recognition of handwritten text that enables auto-correction of the recognized text.
[00100] The present disclosure provides a system and method for the recognition of handwritten text that enables prediction of letters and words for the recognized text.
[00101] The present disclosure provides a system and method for the recognition of handwritten text that enables pronunciation of the recognized word as well as the meaning of the recognized text.
[00102] The present disclosure provides a system and method for the recognition of handwritten text that is cost effective and easy to implement.
[00103] The present disclosure provides a system and method for the recognition of handwritten text that enhances adaptability to new handwritten texts.

We Claim:
1. A handwritten text recognition system, said system comprising:
an input device to receive a sample of handwritten content as input;
one or more processors of a computing device, the one or more processors operatively coupled to the input device; and
a memory device coupled with the one or more processors, the memory device storing a set of instructions executable by the one or more processors to:
receive the input sample of handwritten content;
extract one or more visual features from the received sample of handwritten content to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language;
determine one or more letters of the language by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language; and
predict, for the determined one or more letters, the following combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters.
2. The system as claimed in claim 1, wherein the input device is selected from a group comprising, a touchpad, a touch-enabled screen of a computing device, an optical sensor, and an image scanner.
3. The system as claimed in claim 1, wherein the language comprises Gurmukhi or Punjabi language.
4. The system as claimed in claim 1, wherein combination of the one or more letters and the one or more characters determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
- 21 -
5. The system as claimed in claim 1, wherein the system comprising a presentation unit operatively coupled to the one or more processors, the presentation unit configured to present the predicted following combination of one or more letters and one or more characters.
6. The system as claimed in claim 5, wherein presentation by the presentation unit comprises any or a combination of audio pronunciation and visual display of the predicted following combination of one or more letters and one or more characters.
7. The system as claimed in claim 5, wherein the presentation unit configured to present dictionary meaning of the predicted following combination of one or more letters and one or more characters based on a fifth dataset comprising words of the language and the dictionary meaning of the words of the language.
8. A method for recognizing hand-written texts, said method comprises:
receiving, by an input device, a sample of handwritten content as input;
receiving, by one or more processors, the input sample of handwritten content;
extracting, by the one or more processors, the one or more visual features from the received sample of handwritten content to determine one or more characters of a language by comparing at least a part of the extracted one or more visual features with a first dataset comprising a set of visual features for the one or more characters of the language;
determining, by the one or more processors, one or more letters of the language by comparing at least a part of the determined one or more characters with a second dataset comprising one or more characters pertaining to the one or more letters of the language; and
predicting, by the one or more processors, for the determined one or more letters, a following combination of one or more letters and one or more characters based on a third dataset comprising historical occurrence of a corresponding combination of one or more letters and one or more characters following the determined one or more letters.
9. The method as claimed in claim 8, wherein combination of the one or more letters and the one or more characters are used to determine one or more words of the language, and wherein the one or more processors configured to predict combination of the one or more words based on a fourth dataset comprising historical occurrence of a corresponding combination of one or more words following the determined one or more words.
10. The method as claimed in claim 8, wherein the method comprising a presentation unit operatively coupled to the one or more processors, the presentation unit configured to
- 22 -
present the predicted following combination of one or more letters and one or more characters.