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1 unknown mode: new |
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2 .login: No such file or directory |
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3 % ocrchie |
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4 ocrchie |
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5 |
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6 Done initializing new tcl commands |
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7 Done initializing link variables |
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8 Learning from train.tif and train.txt sych 0 |
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9 open succeeded on file train.tif. length = 300. width = 2400 |
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10 Extracting Components |
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11 |
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12 @@ |
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13 @ |
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14 Learning from 4.header.tif and 4.header.txt sych 1 |
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15 open succeeded on file 4.header.tif. length = 129. width = 1825 |
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16 Extracting Components |
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17 |
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18 Opening train.tif |
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19 open succeeded on file train.tif. length = 300. width = 2400 |
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20 SCALE_FACTOR = 1.000000 Skip = 1.000000 |
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21 Starting deskew process: time = 0.000000 |
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22 Determining Line boundaries |
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23 Calling recognize from Tcl |
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24 Extracting Components |
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25 Recognizing document |
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26 abcdefghijklmnopqrstuvwxyz: ;\ |
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27 ABCDEFGHIJKLMNOPQRSTUVWXYZ |
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28 0 1 234567890~ ! @#$%%^&* ()+=-,.<<>>/?' |
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29 Error: Huh?Opening 4.col0.tif |
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30 open succeeded on file 4.col0.tif. length = 2461. width = 877 |
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31 SCALE_FACTOR = 1.000000 Skip = 1.000000 |
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32 Starting deskew process: time = 0.000000 |
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33 Determining Line boundaries |
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34 Calling recognize from Tcl |
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35 Extracting Components |
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36 Recognizing document |
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37 dispersion and models of temporal price dis- |
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38 persion. Most models of spatial price disper- |
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39 sion, such as the Salop-Stiglitz model or the |
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40 Wilde-Schwartz model, have equilibria with |
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41 specific prices being charged with positive |
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42 probability mass. The above argument shows |
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43 that such strategies cannot be profit-max- |
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44 iMzing Nash behavior in a temporal ran- |
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45 domizing model. |
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46 Since there are no point masses in the |
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47 equilibrium density, the cumulative distribuM |
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48 tion function will be a continuous function |
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49 On (P*, T ). Let E(P ) be the cumuIative dis- |
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50 tribution function for y( P ); thus y(P) = |
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51 E^(P ) almost eveWhere. |
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52 We can now construct the expected profit |
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53 function for a representative store. When a |
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54 store charges price P, exactIy two events are |
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55 relevant. lt may be that P is the smallest |
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56 price heing charged, in which case, the given |
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57 store gets all of the informed customers. |
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58 nis event happens only if all the other |
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59 8tores charge prices higher than P, an event |
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60 which has probability (l - F( P ))*- ' . On the |
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61 other hand, there may be some store with a |
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62 lower price, in which case the store in ques- |
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63 tion only gets its share of the uninformed |
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64 customers. This event happens with proba- |
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65 bility l - (l - F( P))^ - '. (By Proposition 3 |
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66 we can neglect the prohahiIity of any ties.) |
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67 Hence the expected profit of a representa- |
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68 . |
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69 tIve store Is |
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70 /: ( N( P )( l - E( P ))^- ' |
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71 |
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72 + @( P ) ( l - ( l - F( P ))^- ' ) 1/( P ) @ |
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73 where N( P ) =P( C+ I ) - C( C+ I ) |
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74 HI( P ) -P C- %( C ) |
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75 The maximization problem of the firm is |
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76 to choose the density function y( P) so as to |
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77 maximize expected profits su%ect to the |
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78 constraInts: |
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79 /V |
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80 1( P ) ? 0 ; .y( P ) @ - l |
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81 It is clear that all prices that are charged |
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82 with positive density must yield the same |
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83 Error: Huh?Opening hal4eqn.tif |
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84 open succeeded on file hal4eqn.tif. length = 1349. width = 1753 |
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85 SCALE_FACTOR = 1.000000 Skip = 1.000000 |
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86 Starting deskew process: time = 0.000000 |
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87 Determining Line boundaries |
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88 109.0 449.0 3 354 538 |
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89 109.0 449.0 0 61 89 |
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90 1622.0 719.0 4 641 780 |
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91 1622.0 719.0 3 354 538 |
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92 390.0 913.0 5 878 961 |
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93 390.0 913.0 4 641 780 |
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94 1450.0 919.0 5 878 961 |
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95 1450.0 919.0 5 878 961 |
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96 401.0 1108.0 6 1059 1154 |
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97 401.0 1108.0 5 878 961 |
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98 1095.0 1104.0 6 1059 1154 |
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99 1095.0 1104.0 6 1059 1154 |
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100 Calling recognize from Tcl |
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101 Extracting Components |
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102 Recognizing document |
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103 we can neglect the probability ot any ties.) |
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104 Hence the expected profit of a representa- |
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105 tive store is |
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106 / P ^. ' ^ * ' ^ * ' ' - ^ ' ^ * * ^ - ' |
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107 + w 1 ( P ) ( l - ( l - E ( P ) ) ^ - ' ) ) y ( P ) d T |
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108 where w S ( P ) - - P ( C + I ) - C ( C + I ) |
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109 E 1 ( P ) - - P G - C ( C ) |
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110 The maximization problem of the firm is |
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111 Calling recognize from Tcl |
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112 Extracting Components |
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113 Recognizing document |
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114 we can neglect the probability ot any ties.) |
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115 Hence the expected profit of a representa- |
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116 tive store is |
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117 / P ^. ' ^ * ' ^ * ' ' - ^ ' ^ * * ^ - ' |
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118 + w 1 ( P ) ( l - ( l - E ( P ) ) ^ - ' ) ) y ( P ) d T |
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119 where w S ( P ) - - P ( C + I ) - C ( C + I ) |
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120 E 1 ( P ) - - P G - C ( C ) |
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121 The maximization problem of the firm is |
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122 Calling interactive learn from Tcl |
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123 Learning from recog.tmp and 1 sych 0 |
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124 Calling recognize from Tcl |
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125 Extracting Components |
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126 Recognizing document |
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127 we can neglect the probability of any ties.) |
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128 Hence the expected profit of a representa- |
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129 tive store is |
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130 <Int> P r* { <pi> s ( p ) ( 1 - F ( p ) ) n - 1 |
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131 + <pi> f ( p ) [ l - ( l - F ( P ) ) n - 1 ] } f ( p ) d p |
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132 where <pi> s ( P ) = - P ( + + I ) - C ( + + I ) |
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133 <pi> f ( p ) = - U - c C ( ) ) |
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134 The maximization problem of the firm is |
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135 Calling recognize from Tcl |
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136 Extracting Components |
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137 Recognizing document |
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138 we can neglect the probability of any ties.) |
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139 Hence the expected profit of a representa- |
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140 tive store is |
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141 <Int> P r* { <pi> s ( p ) ( 1 - F ( p ) ) n - 1 |
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142 + <pi> f ( p ) [ l - ( l - F ( P ) ) n - 1 ] } f ( p ) d p |
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143 where <pi> s ( P ) = - P ( + + I ) - C ( + + I ) |
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144 <pi> f ( p ) = - U - c C ( ) ) |
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145 The maximization problem of the firm is |
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146 436.9u 302.5s 14:08 87% |
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147 % ocrchie |
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148 ocrchie |
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149 |
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150 Done initializing new tcl commands |
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151 Done initializing link variables |
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152 Learning from train.tif and train.txt sych 0 |
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153 open succeeded on file train.tif. length = 300. width = 2400 |
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154 Extracting Components |
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155 |
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156 @@ |
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157 @ |
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158 Learning from 4.header.tif and 4.header.txt sych 1 |
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159 open succeeded on file 4.header.tif. length = 129. width = 1825 |
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160 Extracting Components |
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161 @ @ |
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162 Opening train.tif |
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163 open succeeded on file train.tif. length = 300. width = 2400 |
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164 SCALE_FACTOR = 1.000000 Skip = 1.000000 |
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165 Starting deskew process: time = 0.000000 |
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166 Determining Line boundaries |
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167 Calling recognize from Tcl |
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168 Extracting Components |
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169 Recognizing document |
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170 Segmentation fault (core dumped) |
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171 11.5u 4.7s 0:33 48% |
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172 % Segmentation fault (core dumped) |
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173 Segmentation fault (core dumped) |
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174 |
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175 Badly placed ()'s. |
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176 % rm core |
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177 rm core |
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178 |
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179 % run |
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180 run |
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181 |
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182 run: Command not found. |
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183 % ocrchie |
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184 ocrchie |
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185 |
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186 Done initializing new tcl commands |
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187 Done initializing link variables |
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188 Learning from train.tif and train.txt sych 0 |
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189 open succeeded on file train.tif. length = 300. width = 2400 |
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190 Extracting Components |
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191 |
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192 @@ |
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193 @ |
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194 Learning from 4.header.tif and 4.header.txt sych 1 |
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195 open succeeded on file 4.header.tif. length = 129. width = 1825 |
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196 Extracting Components |
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197 |
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198 Opening int.tif |
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199 open succeeded on file int.tif. length = 227. width = 277 |
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200 SCALE_FACTOR = 1.000000 Skip = 1.000000 |
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201 Starting deskew process: time = 0.000000 |
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202 Determining Line boundaries |
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203 Calling recognize from Tcl |
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204 Extracting Components |
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205 Recognizing document |
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206 /: ' ^ |
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207 Thank you for using OCRchie. |
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208 16.0u 12.4s 0:35 81% |
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209 % grep Extracting Components {*.cc *.tcl} |
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210 grep Extracting Components {*.cc *.tcl} |
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211 |
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212 Missing }. |
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213 % grep Extracting Components *.cc |
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214 grep Extracting Components *.cc |
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215 |
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216 grep: can't open Components |
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217 Page.cc: printf("Extracting Components\n"); |
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218 status_message.cc: set_string_status("Extracting Words", p, f); |
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219 status_message.cc: set_string_status("Extracting Characters", p, f); |
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220 % |