udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

dannguyen

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

bishboria

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

myungsub

1. Expressing an Image Stream with a Sequence of Natural Sentences

• Cesc Park, Seoul National University; Gunhee Kim*, Seoul National University

2. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [Paper]

• Shaoqing Ren, USTC; Kaiming He*, Microsoft Research Asia; Ross Girshick, Microsoft Research; Jian Sun, Microsoft Research Asia

3. Space-Time Local Embeddings

• Ke SUN*, University of Geneva; Jun Wang, Expedia, Geneva; Alexandros Kalousis, ; Stephane Marchand-Maillet, University of Geneva

4. Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning

• Jiajun Wu*, MIT; Ilker Yildirim, MIT; William Freeman, MIT; Josh Tenenbaum, MIT

5. Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets [Paper]

• Armand Joulin*, Facebook AI research; Tomas Mikolov, Facebook AI Research

nervetumer

#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2015 Nervana Systems Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

myungsub

Vision & Language

• Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images

  • Mateusz Malinowski, Marcus Rohrbach, Mario Fritz

• Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books

  • Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler

• Learning Query and Image Similarities With Ranking Canonical Correlation Analysis

• Wah Ngo

jdeng

  // generate [0..n-1]
  auto seq = [](size_t n) -> std::vector<size_t> {
      std::vector<size_t> v(n);
      for (size_t i=0; i<n; ++i) v[i] = i;
      return v;
  };
  auto index = seq(n);

  // n * n distance matrix
  std::vector<D> dists(n * n);

alextp

# coding: utf-8

import theano
import theano.tensor as T
import theano.sparse

import numpy as np


class Arow(object):