familia restricta, fortasse ad Aelamiticam exstinctam appropinquanda; a nonnullis in superfamiliam Nostraticam inclusa
Divisiones:
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ISO 639-2:
dra
Familiae linguisticae coloribus Vicipaedicis pictae
Forma linguarum Dravidicarum
Linguae Dravidicae[1] sunt sermones cognati ab aliis omnibus linguis vel segregatae vel longe distantes, qui in paeninsula Indica, praecipue meridiana, nec non in Srilanca adhibentur. Ad hanc familiam pertinent lingua Tamulica (per duo millennia litteris inscriptionibusque cognita) aliaeque multae. Linguam Aelamiticam, millennio I a.C.n. in Perside litteris cuneiformibus inscriptam et nunc exstinctam, eruditi nonnulli familia Dravidica cognatam asseverant.
Index
1Taxinomia
2Notae
3Bibliographia
4Nexus interni
5Nexus externi
Taxinomia |
divisio boreoccidentalis
Brahui
divisio boreorientalis
Kurukh, Malto
divisio centralis
Kolami, Naiki, Gadaba, Parji
divisio australocentralis
Lingua Gondvanensis, Koi, Maria, Konda, Kuba, Kui, Kuvi, Manda, Pengo
Lingua Telingana, Chenchu, Holiya, Savara
divisio australis
Lingua Tuluva, Koraga, Mudu
Lingua Cannadica, Badaga, Kurumba
Toda, Kota
Kodagu (vel Coorgi)
Irula, Yerukala (vel Kodava)
Lingua Malabarica, Paniya
Lingua Tamulica
Notae |
↑Olim "linguae Dekhanicae": Christianus Lassen, Institutiones linguae Pracriticae (Bonnae, 1837) textus appendix p. 9.
Bibliographia |
Robert Caldwell, A comparative grammar of the Dravidian or South-Indian family of languages. London: Harrison, 1856; anno 1998 reimpressum. ISBN 81-206-0117-3
Vishnupriya Kolipakam et al., "A Bayesian phylogenetic study of the Dravidian language family" in Royal Society Open Science (21 Martii 2018)
Bhadriraju Krishnamurti, The Dravidian Languages. Cambridge: Cambridge University Press, 2003. ISBN 0-521-77111-0
Sanford B. Steever, ed., The Dravidian Languages. Londinii: Routledge, 1998
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
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This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
