How do I convert Marathi or Hindi books so that I can read it on Kindle?
You should first of course have the soft copies (.pdf .doc etc.) of Hindi or Marathi books. If you don have a soft copy you can scan those books store them as s) and convert them to editable s using Optical Character Recognition (OCR) tools. You can get it done for free here i2OCR - Free Online OCR Once you have a soft copy you have to convert it to AZW AZW 3 format to read it on Kindle. That can be done here Online ebook converter s Happy reading! -)
How can I enter and survive in an Indian medical school if I have motor dysgraphia? I have good reading, understanding, and remembering ability. I also have discipline and a strong desire to become a good science doctor since early childhood.
Have you heard of Bolotin? No. It's okay. Find more about him. If yes Good for you. I read about him in my 1th standard SSC board Marathi book. And since then I stopped giving excuses. He is the first Blind practicing Physician in the world. Now you and I can both imagine how difficult it must be to be a blind doctor because medicine is all about observing and to observe you need eyes. From pallor of your skin and conjunctiva to diagnose anaemia to clubbing on fingernails to diagnose multiple heart and lung related conditions you need eyes. And still Dr. Bolotin became a physician without having the ability to see. Now I have no idea how severe your motor dysgraphia is but I believe you can be a doctor if you put your heart in it. Study Hard. That is what is required. You would require practicing filling circles properly though. Because NEET is a OCR based exam where the machine scans your answers which you mark by filling small circles. This is all to get in. You would find some difficulty to sit for your 12th boards but I have no idea which class you are in. If you have cleared what ever class you are in till now I feel you can survive 12th too. Coming to MBBS. That is where your difficulty will start and you will definitely get your greatest support. Your teachers will definitely support you alot. Almost all teachers are very considerate. You will have problems in subject like Anatomy Pathology and Surgery which require you to draw diagrams. Rest of the subjects require you to draw 132 diagrams in each paper which I feel you would manage by practicing a little more than your peers. Anatomy would be your toughest subject as you will be required to do dessections. And my answer to that my friend is practice and remember that you can see Dr. Bolotin couldn't even do that. Last but not the least don't take offense with what I am suggesting but I feel you should legalise your disablity if you haven't already. That means get a disablity certificate. I have worked in Government Hospital's disablity clinic and you can actually fill application online and get a appointment with the assigned doctor at your nearest government hospital. He will provide you better gance as it would be face to face. Even if you don't get educational concessions with your certificate you can use it to apply for writers. That will decrease your manual work by half. Then you just have to focus on learning. If I knew what you are pursuing educationally right now I feel I could ge you better. I hope you pursue your dream of bing a doctor and I hope you be one. Good luck!
How does Google Translate work to translate one language to another language?
Translation methodology In April 26 Google Translate launched with a statistical machine translation engine. Google Translate does not apply grammatical rules since its algorithms are based on statistical or pattern analysis rather than traditional rule-based analysis. The system's original creator Franz Josef Och has criticized the effectiveness of rule-based algorithms in favor of statistical Original versions of Google Translate were based on a method called statistical machine translation and more specifically on research by Och who won the DARPA contest for speed machine translation in 23. Och was the head of Google's machine translation group until leaving to join Human Longevity Inc. in July 214. Google Translate does not translate from one language to another (L1 2 L2). Instead it often translates first to English and then to the target language (L1 2 EN 2 L2).However because English like all human languages is ambiguous and depends on con this can cause translation errors. For example translating vous from French to Russian gives vous 2 you 2 u442u44b OR Bu44b If Google were using an unambiguous artificial language as the intermediary it would be vous 2 you 2 Bu44b OR tu 2 thou 2 u442u44b. Such a suffixing of words disambiguates their different meanings. Hence publishing in English using unambiguous words providing con using expressions such as you all often make a better one-step translation. The following languages do not have a direct Google translation to or from English. These languages are translated through the indicated intermediate language (which in most cases is closely related to the desired language but more widely spoken) in addition to through English Belarusian (be 4 ru 4 en 4 other) Catalan (ca 4 es 4 en 4 other) Galician (gl 4 pt 4 en 4 other) Haitian Creole (ht 4 fr 4 en 4 other) Korean (ko 4 jp 4 en 4 other) Slovak (sk 4 cz 4 en 4 other) Ukrainian (uk 4 ru 4 en 4 other) Urdu (ur 4 hi 4 en 4 other) According to Och a solid base for developing a usable statistical machine translation system for a new pair of languages from scratch would consist of a bilingual corpus (or parallel collection) of more than 15-2 million words and two monolingual corpora each of more than a billion models from these data are then used to translate between those languages. To acquire this huge amount of linguistic data Google used United Nations and European Parliament documents and UN typically publishes documents in all six official UN languages which has produced a very large 6-language corpus. When Google Translate generates a translation proposal it looks for patterns in hundreds of millions of documents to help decide on the best translation. By detecting patterns in documents that have already been translated by human translators Google Translate makes informed guesses (AI) as to what an appropriate translation should be. Before October 27 for languages other than Arabic Chinese and Russian Google Translate was based on SYSTRAN a software engine which is still used by several other online translation services such as Babel Fish (now defunct). From October 27 Google Translate used proprietary in-house technology based on statistical machine translation insteadbefore transitioning to neural machine translation. Google Translate Community Google has crowdsourcing features for volunteers to be a part of its Translate Community intended to help improve Google Translate's August 216 a Google Crowdsource app was released for Android users in which translation tasks are are three ways to contribute. First Google will show a phrase that one should in the translated Google will show a proposed translation for a user to agree disagree or skip. Third users can suggest translations for phrases where they think they can improve on Google's results. Tests in 44 languages show that the suggest an edit feature led to an improvement in a maximum of 4% of cases over four years while analysis across the board shows that Google's crowd procedures often lock in erroneous translations. Statistical machine translation Although Google deployed a new system called neural machine translation for better quality translation there are languages that still use the traditional translation method called statistical machine translation. It is a rule-based translation method that utilizes predictive algorithms to guess ways to translate s in foreign languages. It aims to translate whole phrases rather than single words then gather overlapping phrases for translation. Moreover it also analyzes bilingual corpora to generate statistical model that translates s from one language to Google Neural Machine Translation Main article Google Neural Machine Translation In September 216 a research team at Google led by the software engineer Harold Gilchrist announced the development of the Google Neural Machine Translation system (GNMT) to increase fluency and accuracy in Google Translate and in November announced that Google Translate would switch to GNMT. Google Translate's neural machine translation system uses a large end-to-end artificial neural network that attempts to perform deep learningin particular long short-term memory improves the quality of translation over SMT in some instances because it uses an example-based machine translation (EBMT) method in which the system learns from millions of examples.9 According to Google researchers it translates whole sentences at a time rather than just piece by piece. It uses this broader con to help it figure out the most relevant translation which it then rearranges and adjusts to be more like a human speaking with proper grammar. GNMT's proposed architecture of system learning has been implemented on over a hundred languages supported by Google Translate.9 With the end-to-end framework Google states but does not demonstrate for most languages that the system learns over time to create better more natural GNMT network attempts interlingual machine translation which encodes the semantics of the sentence rather than simply memorizing phrase-to-phrase translationsand the system did not invent its own universal language but uses themonality found in between many was first enabled for eight languages to and from English and Chinese French German Japanese Korean Portuguese Spanish and Turkish. In March 217 it was enabled for Hindi Russian and Vietnamese followed by Bengali Gujarati Indonesian Kannada Malayalam Marathi Punjabi Tamil and Telugu in April. Accuracy Google Translate is not as reliable as human translation. When is well-structured written using formal language with simple sentences relating to formal topics for which training data is ample it often produces conversions similar to human translations between English and a number of high-resource decreases for those languages when fewer of those conditions apply for example when sentence length increases or the uses familiar or literary language. For many other languages vis-ue-vis English it can produce the gist of in those formal evaluation from English to all 12 languages shows that the main idea of a is conveyed more than 5% of the time for 35 languages. For 67 languages a minimallyprehensible result is not achieved 5% of the time or greater.A few studies have evaluated Chinesecitation needed Frenchcitation needed Germancitation needed and Spanishcitation needed to English but no systematic human evaluation has been conducted from most Google Translate languages to English. Speculative language-to-language scores extrapolated from English-to-other measurements99 indicate that Google Translate will produce translation results that convey the gist of a from one language to another more than half the time in about 1% of language pairs where neither language is English. When used as a dictionary to translate single words Google Translate is highly inaccurate because it must guess between polysemic words. Among the top 1 words in the English language which make up more than 5% of all written English the average word has more than 15 senses which makes the odds against a correct translation about 15 to 1 if each sense maps to a different word in the target language. Mostmon English words have at least two senses which produces 5 odds in the likely case that the target language uses different words for those different senses. The odds are similar from other languages to English. Google Translate makes statistical guesses that raise the likelihood of producing the most frequent sense of a word with the consequence that an accurate translation will be unobtainable in cases that do not match the majority or plurality corpus occurrence. The accuracy of single-word predictions has not been measured for any language. Because almost all non-English language pairs pivot through English the odds against obtaining accurate single-word translations from one non-English language to another can be estimated by multiplying the number of senses in the source language with the number of senses each of those terms have in English. When Google Translate does not have a word in its vocabulary it makes up a result as part of its algorithm. Limitations Google Translate like other automatic translation tools has its limitations. The service limits the number of paragraphs and the range of technical terms that can be translated and while it can help the reader understand the general content of a foreign language it does not always deliver accurate translations and most times it tends to repeat verbatim the same word it's expected to translate. Grammatically for example Google Translate struggles to differentiate between imperfect and perfect aspects in Romance languages so habitual and continuous acts in the past often be single historical events. Although seemingly pedantic this can often lead to incorrect results (to a native speaker of for example French and Spanish) which would have been avoided by a human translator. Knowledge of the subjunctive mood is virtually the formal second person (vous) is often chosen whatever the con or accepted its English reference material contains only you forms it has difficulty translating a language with you all or formal you variations. Due to differences between languages in investment research and the extent of digital resources the accuracy of Google Translate varies greatly among languages produce better results than others. Most languages from Africa Asia and the Pacific tend to score poorly in relation to the scores of many well-financed European languages with Afrikaans and Chinese being the high-scoring exceptions from their languages indigenous to Australia or the Americas are included within Google Translate. Higher scores for European can be partially attributed to the Europarl Corpus a trove of documents from the European Parliament that have been professionally translated by the mandate of the European Union into as many as 21 languages. A 21 analysis indicated that French to English translation is relatively accurate and 211 and 212 analyses showed that Italian to English translation is relatively accurate as if the source is shorter rule-based machine translations often perform better; this effect is particularly evident in Chinese to English translations. While edits of translations may be submitted in Chinese specifically one cannot edit sentences as a whole. Instead one must edit sometimes arbitrary sets of characters leading to incorrect A good example is Russian-to-English. Formerly one would use Google Translate to make a draft and then use a dictionary andmon sense to correct the numerous mistakes. As of early 218 Translate is sufficiently accurate to make the Russian Wikipedia accessible to those who can read English. The quality of Translate can be checked by adding it as an extension to Chrome or Firefox and applying it to the left language s of any Wikipedia article. It can be used as a dictionary by typing in words. One can translate from a book by using a scanner and an OCR like Google Drive but this takes about five minutes per page. In its Written Words Translation function there is a word limit on the amount of that can be translated at Therefore long should be transferred to a document form and translated through its Document Translate function. Moreover like all machine translation programs Google Translate struggles with polysemy (the multiple meanings a word may have)1913 and multiword expressions (terms that have meanings that cannot be understood or translated by analyzing the individual word units thatpose them).11 A word in a foreign language might have two different meanings in the translated language. This might lead to mistranslations. Additionally grammatical errors remain a major limitation to the accuracy of Google Translate. Thanks for reading till here.