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android ocr library tutorial: May 4, 2018 · Now it is very easy with the help of Google Mobile Vision API which is very powerful and reliable Optic ...android ocr tutorial See and Understand Text using OCR with Mobile Vision Text API for ...android ocr pdf, java ocr pdf documents, best online ocr software for chinese characters, html5 camera ocr, pdf ocr software open source, windows tiff ocr, ocrsdk forum, ocr library ios, c# ocr windows 10, ocr activex free, php ocr api, ocr software open source linux, open source ocr software mac os x, sharepoint ocr search, tesseract ocr python windows android arabic ocrAndroid OCR apps can convert scanned images to text, and that is coming in handy in this ... PDF Scanner is one of the eye-catching OCR apps for Android. android ocr library exampleApr 13, 2018 · We go over what we believe to be are the top 5 OCR APIs available at ... The free tier for Microsoft's API will give you 5,000 requests per month. for a malicious anchor to determine the location of the request coming in (by using techniques such as time-of- ight) and send out the normal beacon if the location corresponds to an anchor node This also assumes that a malicious anchor node is aware of the locations of other anchor nodes The authors in this work ignore this, though, by assuming that a malicious anchor node would not be able to differentiate between the requests coming from an anchor node or a nonanchor node Another approach for dealing with malicious anchor nodes is given in [170] They present two different ideas to deal with this problem The rst is based on the observation that every node typically uses the mean square error calculation to estimate its location The outliers can then be ltered using the calculated error To exploit this observation a node rst determines its location using the MMSE-based method and information from all the anchors it hears from The node then assesses if the estimated location is consistent with the information transmitted by all the anchors If the estimated distance is consistent then the process terminates, else the node attempts to identify and remove the most inconsistent reference and repeats the process The process continues until either a set of consistent location references is obtained or a conclusion is reached that such a set is impossible to nd (since the number of location references is less than three) Given a set L of anchors and a threshold to be used to determine inconsistency, a naive approach to computing the largest set of consistent location references is to check all subsets of L starting with the entire set This could be done until a subset of L is found that is consistent or it can be concluded that it is not possible to nd such a subset As is obvious, this will be computationally inef cient, which is a big disadvantage given the resource constraints on nodes in ad hoc networks To address this, the authors in [170] also propose a greedy algorithm which works n rounds In the rst round, the set of all location references is used to verify that they are consistent If these are consistent then the algorithm outputs the estimated location and terminates Otherwise, the algorithm considers all subsets of location references with one fewer location reference than in the previous round and chooses the subset with the least mean square error as the input to the next round The algorithm continues until a subset is found that is consistent or it can be concluded that it is not possible to nd such a subset The second idea [170] is to use an iteratively re ned voting scheme in order to tolerate the malicious location information The target eld is divided into a grid of cells Each node determines the likelihood of being present in each cell using the information in the beacons received by it Each cell in which the sensor node can be present is given a vote Finally, the cell(s) with the highest votes is selected and the center of this cell(s) is the estimated location Note that both these ideas assume that the number of benign beacons is more than that of the malicious beacons Thus, in order to defeat these schemes, an attacker will have to ensure that the number of malicious beacon signals is more than the number of benign beacon signals Another way for the adversary to defeat these schemes is by ensuring that he is not too aggressive Note that this might result in some falsi cation of the location determination, which might not be signi cant Another approach that does not consider any speci c attacks but rather focuses on ensuring that statistical robustness is introduced in the computation phase of the localization process is taken in [171] The advantage of this approach is that it achieves robustness against novel and unforeseen attacks The authors illustrate here that the impact of outliers can be limited by employing a least median squares (LMS) technique Note. android ocr scanner github: Choose the Right On-Device Text Recognition ( OCR ) SDK on ... ocr example in android studioGo to unzip your target *.apk file,check if there is a libs folder of which contains the *.so files.If this is your problem,check this link I have ... tesseract ocr android See and Understand Text using OCR with Mobile Vision Text API for ...
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Android OCR application to obtain definition of text scanned. - amoghj8/ OCR - Dictionary. ocr codelab android Android Character Recognition - CodeProject
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GS1 Composite Barcode Information. .Related: for an unlimited number of processors Nevertheless, it is often proposed as an initial step in scheduling for a limited number of processors To distinguish between the limited number of (physical) processors and the unlimited number of (virtual) processors assumed in the clustering step, the latter are called clusters, hence the term clustering In clustering, the nodes of the task graph are mapped and scheduled into these clusters De nition 53 (Clustering) Let G = (V, E, w, c) be a task graph A clustering C is a schedule of G on an implicit parallel system C (De nition 43) with an unlimited number of processors; that is, |C| = |V| The processors C C are called clusters Clustering based scheduling algorithms for a limited number of processors consist of several steps, similar to the two-phase scheduling outlined by Algorithm 13 in Section 52 Algorithm 13 comprises two phases, where the rst phase is the mapping of the nodes and the second is their scheduling In clustering based algorithms, three steps are necessary: (1) clustering, (2) mapping of the clusters to the (physical) processors, and (3) scheduling of the nodes Algorithm 14 outlines a generic three-step clustering based scheduling heuristic for a limited number of processors Algorithm 14 Generic Three-Step Clustering Based Scheduling Algorithm (G = (V, E, w, c), P) Clustering nodes (1) Find a clustering C of G Mapping clusters to processors (2) Assign clusters of C to (physical) processors of P Scheduling/ordering nodes (3) Attribute start time ts (n) to each node n V, adhering to Condition 41 (processor constraint) and Condition 42 (precedence constraint) In contrast to the two-phase Algorithm 13, where the rst step is the pure processor allocation of the nodes, clustering also includes the scheduling of the nodes in the clusters As will be seen later, this is done for an accurate estimation of the execution time of the task graph It also makes clustering a complete scheduling algorithm for an unlimited number of processors hile the third step is theoretically identical to the second phase of Algorithm 13, its actual implementation may differ, since the partial node orders as established by the clustering C might be considered in determining the nal node order In terms of parallel programming terminology, clustering correlates to the step of parallelization designated by orchestration (Culler and Singh [48]) or agglomeration (Foster [69]) as described in Section 23 The often cited motivation for clustering was given by Sarkar [167]: if tasks are best executed in the same processor (cluster) of an ideal system, that is, a system that possesses more processors (clusters) than tasks, they should also be executed on the same processor in any real system Due to the NP-hardness of scheduling, it cannot be. 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26 Feb 2018 ... OCR in Android devices: Create a project on Android Studio with one blank Activity. Add permission for camera in the manifest file : In the MainActivity, check if camera-permission is available or not. On receiving the permission, create a TextRecognizer object. Create a CameraSource object to start the camera. tensorflow ocr android The Best Apps for Mobile Scanning and OCR - Zapier
3 Sep 2018 ... After testing close to 20 mobile scanning and OCR apps , one came .... Available on: Android , iOS, (also available on Windows and Hololens) ... c ocr library: Clara OCR - Open source OCR in C GPL; Cuneiform - CuneiForm OCR was ... Free Online OCR and OCR API by @a9t9 based on Te ...
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