unknown mode: new

.login: No such file or directory

% ocrchie

ocrchie

Done initializing new tcl commands

Done initializing link variables

Learning from train.tif and train.txt sych 0

open succeeded on file train.tif.  length = 300. width = 2400

Extracting Components

@��@	�� 

@      

Learning from 4.header.tif and 4.header.txt sych 1

open succeeded on file 4.header.tif.  length = 129. width = 1825

Extracting Components

Opening train.tif

open succeeded on file train.tif.  length = 300. width = 2400

SCALE_FACTOR = 1.000000  Skip = 1.000000

Starting deskew process: time = 0.000000

Determining Line boundaries

Calling recognize from Tcl

Extracting Components

Recognizing document

abcdefghijklmnopqrstuvwxyz: ;\ 

ABCDEFGHIJKLMNOPQRSTUVWXYZ 

0 1 234567890~ ! @#$%%^&* ()+=-,.<<>>/?' 

Error: Huh?Opening 4.col0.tif

open succeeded on file 4.col0.tif.  length = 2461. width = 877

SCALE_FACTOR = 1.000000  Skip = 1.000000

Starting deskew process: time = 0.000000

Determining Line boundaries

Calling recognize from Tcl

Extracting Components

Recognizing document

dispersion and models of temporal price dis- 

persion. Most models of spatial price disper- 

sion, such as the Salop-Stiglitz model or the 

Wilde-Schwartz model, have equilibria with 

specific prices being charged with positive 

probability mass. The above argument shows 

that such strategies cannot be profit-max- 

iMzing Nash behavior in a temporal ran- 

domizing model. 

Since there are no point masses in the 

equilibrium density, the cumulative distribuM 

tion function will be a continuous function 

On (P*, T ). Let E(P ) be the cumuIative dis- 

tribution function for y( P ); thus y(P) = 

E^(P ) almost eveWhere. 

We can now construct the expected profit 

function for a representative store. When a 

store charges price P, exactIy two events are 

relevant. lt may be that P is the smallest 

price heing charged, in which case, the given 

store gets all of the informed customers. 

nis event happens only if all the other 

8tores charge prices higher than P, an event 

which has probability (l - F( P ))*- ' . On the 

other hand, there may be some store with a 

lower price, in which case the store in ques- 

tion only gets its share of the uninformed 

customers. This event happens with proba- 

bility l - (l - F( P))^ - '. (By Proposition 3 

we can neglect the prohahiIity of any ties.) 

Hence the expected profit of a representa- 

. 

tIve store Is 

/: ( N( P )( l - E( P ))^- ' 

+ @( P ) ( l - ( l - F( P ))^- ' ) 1/( P ) @ 

where N( P ) =P( C+ I ) - C( C+ I ) 

HI( P ) -P C- %( C ) 

The maximization problem of the firm is 

to choose the density function y( P) so as to 

maximize expected profits su%ect to the 

constraInts: 

/V 

1( P ) ? 0 ; .y( P ) @ - l 

It is clear that all prices that are charged 

with positive density must yield the same 

Error: Huh?Opening hal4eqn.tif

open succeeded on file hal4eqn.tif.  length = 1349. width = 1753

SCALE_FACTOR = 1.000000  Skip = 1.000000

Starting deskew process: time = 0.000000

Determining Line boundaries

109.0 449.0 3 354 538

109.0 449.0 0 61 89

1622.0 719.0 4 641 780

1622.0 719.0 3 354 538

390.0 913.0 5 878 961

390.0 913.0 4 641 780

1450.0 919.0 5 878 961

1450.0 919.0 5 878 961

401.0 1108.0 6 1059 1154

401.0 1108.0 5 878 961

1095.0 1104.0 6 1059 1154

1095.0 1104.0 6 1059 1154

Calling recognize from Tcl

Extracting Components

Recognizing document

we can neglect the probability ot any ties.) 

Hence the expected profit of a representa- 

tive store is 

/ P ^. ' ^ * ' ^ * ' ' - ^ ' ^ * * ^ - ' 

+ w 1 ( P ) ( l - ( l - E ( P ) ) ^ - ' ) ) y ( P ) d T 

where w S ( P ) - - P ( C + I ) - C ( C + I ) 

E 1 ( P ) - - P G - C ( C ) 

The maximization problem of the firm is 

Calling recognize from Tcl

Extracting Components

Recognizing document

we can neglect the probability ot any ties.) 

Hence the expected profit of a representa- 

tive store is 

/ P ^. ' ^ * ' ^ * ' ' - ^ ' ^ * * ^ - ' 

+ w 1 ( P ) ( l - ( l - E ( P ) ) ^ - ' ) ) y ( P ) d T 

where w S ( P ) - - P ( C + I ) - C ( C + I ) 

E 1 ( P ) - - P G - C ( C ) 

The maximization problem of the firm is 

Calling interactive learn from Tcl

Learning from recog.tmp and 1 sych 0

Calling recognize from Tcl

Extracting Components

Recognizing document

we can neglect the probability of any ties.) 

Hence the expected profit of a representa- 

tive store is 

<Int> P r* { <pi> s ( p ) ( 1 - F ( p ) ) n - 1 

+ <pi> f ( p ) [ l - ( l - F ( P ) ) n - 1 ] } f ( p ) d p 

where <pi> s ( P ) = - P ( + + I ) - C ( + + I ) 

<pi> f ( p ) = - U - c C ( ) ) 

The maximization problem of the firm is 

Calling recognize from Tcl

Extracting Components

Recognizing document

we can neglect the probability of any ties.) 

Hence the expected profit of a representa- 

tive store is 

<Int> P r* { <pi> s ( p ) ( 1 - F ( p ) ) n - 1 

+ <pi> f ( p ) [ l - ( l - F ( P ) ) n - 1 ] } f ( p ) d p 

where <pi> s ( P ) = - P ( + + I ) - C ( + + I ) 

<pi> f ( p ) = - U - c C ( ) ) 

The maximization problem of the firm is 

436.9u 302.5s 14:08 87%

% ocrchie

ocrchie

Done initializing new tcl commands

Done initializing link variables

Learning from train.tif and train.txt sych 0

open succeeded on file train.tif.  length = 300. width = 2400

Extracting Components

@��@	�� 

@      

Learning from 4.header.tif and 4.header.txt sych 1

open succeeded on file 4.header.tif.  length = 129. width = 1825

Extracting Components

@� @� ��  
�
� ��  

Opening train.tif

open succeeded on file train.tif.  length = 300. width = 2400

SCALE_FACTOR = 1.000000  Skip = 1.000000

Starting deskew process: time = 0.000000

Determining Line boundaries

Calling recognize from Tcl

Extracting Components

Recognizing document

Segmentation fault (core dumped)

11.5u 4.7s 0:33 48%

% Segmentation fault (core dumped)

Segmentation fault (core dumped)

Badly placed ()'s.

% rm core

rm core

% run

run

run: Command not found.

% ocrchie

ocrchie

Done initializing new tcl commands

Done initializing link variables

Learning from train.tif and train.txt sych 0

open succeeded on file train.tif.  length = 300. width = 2400

Extracting Components

@��@	�� 

@      

Learning from 4.header.tif and 4.header.txt sych 1

open succeeded on file 4.header.tif.  length = 129. width = 1825

Extracting Components

Opening int.tif

open succeeded on file int.tif.  length = 227. width = 277

SCALE_FACTOR = 1.000000  Skip = 1.000000

Starting deskew process: time = 0.000000

Determining Line boundaries

Calling recognize from Tcl

Extracting Components

Recognizing document

/: ' ^ 

Thank you for using OCRchie.

16.0u 12.4s 0:35 81%

% grep Extracting Components {*.cc *.tcl}

grep Extracting Components {*.cc *.tcl}

Missing }.

% grep Extracting Components *.cc

grep Extracting Components *.cc

grep: can't open Components

Page.cc:    printf("Extracting Components\n");

status_message.cc:  set_string_status("Extracting Words", p, f);

status_message.cc:  set_string_status("Extracting Characters", p, f);

%